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Research Collection
Habilitation Thesis
Transcriptomic and proteomic approaches to the discovery ofnew markers of pathology
Author(s): Elia, Giuliano
Publication Date: 2005
Permanent Link: https://doi.org/10.3929/ethz-a-005151527
Rights / License: In Copyright - Non-Commercial Use Permitted
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ETH Library
Transcriptomic and Proteomic Approaches to the Discovery of
New Markers of Pathology
A Habilitation Thesis
Presented by
Dr. Giuliano Elia
Institute of Pharmaceutical Sciences Department of Chemistry and Applied Biosciences
Swiss Federal Institute of Technology Zurich
Zurich, February 2005
Index
Page 1. Abstract 5 2. Introduction 8
2.1. Antibody based tumor targeting 8 2.2. The tumor environment and tumor-associated antigens 10 2.3. Angiogenesis and tumor angiogenesis 13 2.4. Markers of angiogenesis 18
2.4.1. EDB domain of fibronectin 18 2.4.2. Integrins αvβ3 and αvβ5 19 2.4.3. Prostate-specific membrane antigen (PSMA) 20 2.4.4. Endoglin (CD105) 20 2.4.5. VEGF and VEGF-receptor complex 21 2.4.6. CD44 21 2.4.7. Phosphatidyl serine phospholipids 22 2.4.8. Large isoform of tenascin C 22 2.4.9. Magic Roundabout 23 2.4.10. Components of the coagulation cascade 23
2.5. Transcriptomic methods for target identification 26
2.5.1. Evolution of transcriptomics 26 2.5.2. Application of transcriptomics methods to the
identification of new targets for tumor therapy 30
2.6. Proteomic methods for target identification 32 2.6.1. Evolution of proteomics: gel-based methods 32
2.6.1.1. Two-dimensional polyacrylamide gel electrophoresis 32 2.6.1.2. Combination of chromatography and 1D SDS-PAGE 36
2.6.2. Evolution of proteomics: gel-free mass spectrometry-based methods 38 2.6.2.1. Multidimensional protein identification technique (MudPIT) 39 2.6.2.2. Combined fractional diagonal chromatography (COFRADICTM) 39 2.6.2.3. Isotope-coded affinity tags (ICAT) 41 2.6.2.4. iTRAQTM Reagents 43 2.6.2.5. Two-dimensional peptide mapping 44
2.6.3. Ligand-based methods 45
2.6.4. Application of proteomic methods to the identification of new targets for tumor therapy 46
2.7. From in vitro to in vivo model systems for the identification of vascular targets 48
3. Results & Discussion 51
3.1. Modulation of gene expression by extracellular pH variations in human fibroblasts: a transcriptomic and proteomic study 51
3.1.1. Genome-wide analysis of mRNA levels in NHDF cultured at different pH 52
3.1.2. Modulation in the expression of ECM components in NHDF after serum starvation and/or pH change 60
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3.1.3. 2D-PAGE analysis of secreted proteins expressed by NHDF cultured at different pH 62
3.1.4. Discussion 66
3.2. Modulation of gene expression by hypoxia in HUVEC :
a transcriptomic and proteomic study 71 3.2.1. Genome-wide analysis of gene expression modulation
in HUVEC cultured in hypoxic vs. normoxic conditions 74 3.2.2. RT-PCR analysis of expression level and of alternative
splicing for some extracellular matrix protein genes 83 3.2.3. Proteomic study 85 3.2.4. Discussion 96
3.2.4.1. Transcriptomics 97 3.2.4.2. Proteomics 106
3.3. Identification and relative quantification of membrane
proteins by surface biotinylation and two-dimensional peptide mapping 109
3.3.1. Surface biotinylation and two-dimensional peptide mapping for the proteomic study of membrane proteins 111
3.3.2. Detection of tryptic peptides derived from a BSA-spike added to an HEK membrane protein extract 115
3.3.3. Two-dimensional peptide mapping of membrane proteins of HUVEC cell cultures 117
3.3.4. Comparison of membrane protein expression in HUVEC cells exposed to hypoxia and normoxia 118
3.3.5. Discussion 121
3.4. In vivo protein biotinylation for identification of
organ–specific antigens accessible from the vasculature. 125 3.4.1. Terminal perfusion and in vivo biotinylation 127 3.4.2. Histochemical analysis of biotinylated structures
in organ and tumor sections 129 3.4.3. Purification and identification of biotinylated proteins
by proteomic techniques 131 3.4.4. Validation of some candidate marker proteins identified 135 3.4.5. Discussion 138
4. Conclusions and Outlook 147
4.1. Conclusions 147 4.2. Ex vivo protein biotinylation of surgical specimens
from tumor-bearing patients 148 5. Materials and Methods 151
5.1. Cell culture 151 5.1.1. NHDF cell culture 151 5.1.2. HUVEC cell culture 152
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5.1.3. HUVEC and HEK cell culture for 2D peptide mapping experiments 152 5.1.4. F9 teratocarcinoma cells and RENCA cells for in vivo
biotinylation experiments 153
5.2. Transcriptomic methods 154 5.2.1. Northern Blot 154 5.2.2. Oligonucleotide microarray analysis 154 5.2.3. RT-PCR analysis of alternatively spliced transcripts 156
5.3. Gel-based proteomic methods 158 5.3.1. 2 D-PAGE 158 5.3.2. In-gel tryptic digestion of proteins 159
5.4. Mass spectrometry 160 5.4.1. µLC-MS/MS 160 5.4.2. MALDI-TOF/TOF MS 161
5.5. In vitro biotinylation of surface proteins for 2D peptide mapping 162 5.5.1. Biotinylation of standard proteins 162 5.5.2. Biotinylation of cell surface proteins 163 5.5.3. Isolation of biotinylated proteins 163 5.5.4. Digestion of the eluted proteins 164
5.6. Two-dimensional peptide mapping 165 5.6.1. Reversed-phase HPLC 165 5.6.2. Programming of the Spectational software 165 5.6.3. Processing of MALDI-TOF spectra 166
5.7. In vivo biotinylation of proteins accessible from the bloodstream 166 5.7.1. Animal experiments 166 5.7.2. Terminal perfusion of animals and in vivo biotinylation 167 5.7.3. Preparation of protein extracts from organ homogenates 168 5.7.4. Biotinylated protein purification 168 5.7.5. On-resin tryptic digestion of biotinylated proteins 169
5.8. Target validation methods 169 5.8.1. Histochemical analysis of biotinylated structures in
organ and tumor sections 169 5.8.2. Western Blot analysis 170 5.8.3. Immunofluorescence experiments 172 5.8.4. Indirect Immunofluorescence 173
6. Acknowledgements 174 7. References 176 8. Appendix A 229 9. Appendix B 234
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1. Abstract
The identification of new molecular markers of pathology, to be used as target for the
specific delivery of diagnostic or therapeutic agents, is possibly the most important
goal of modern pharmacological research. In this thesis, I will first describe results
obtained by transcriptomic and proteomic high-throughput methods, applied to simple
in vitro model systems, aiming at the identification of new, candidate targets for
cancer therapy.
We have performed a broad-range analysis of the patterns of gene expression in
normal human dermal fibroblasts at two different pH values (in the presence and in
the absence of serum), with the aim of getting a deeper insight in the regulation of the
transcriptional program as a response to a pH change. Using the Affymetrix gene chip
system, we found that the expression of 2,068 genes (out of 12,565) was modulated
by more than two-fold at 24, 48 or 72 hours after the shift of the culture medium pH
to a more acidic value, stanniocalcin 1 being a remarkable example of strongly up-
regulated gene. Genes displaying a modulated pattern of expression included, among
others, cell cycle regulators (consistent with the observation that acidic pH abolishes
the growth of fibroblasts in culture) and relevant extracellular matrix (ECM)
components. Extracellular matrix protein 2, a protein with a restricted pattern of
expression in adult human tissues, was found to be remarkably over expressed,
consequent to serum starvation.
Aiming at getting a detailed insight into the oxygen-dependent regulation of the
transcriptional program of vascular endothelial cells, we have performed a broad-
range transcriptomic analysis, using the Affymetrix HG-U133A Gene Chips, of
mRNA expression levels in human umbilical cord vein endothelial cells (HUVEC),
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exposed in vitro to hypoxia for different time periods. The transcriptomic analysis
was complemented by a semi-quantitative RT-PCR analysis of mRNA levels and
alternative splicing for some selected extracellular matrix protein genes, and by a
proteomic analysis (using 2D-PAGE and tandem mass spectrometry for protein
separation and identification) of hypoxic and normoxic HUVEC whole cell lysates
and sub-cellular fractions. Our analysis confirmed previous findings on genes whose
expression is regulated by oxygen concentration, but also identified new genes (e.g.,
CXCR4, claudin 3, CD24, tetranectin, Del-1, procollagen lysyl hydroxylase 1 and 2)
which are transcriptionally up regulated in hypoxic conditions.
In a second part of this thesis, I will also present a method for the simultaneous
recovery, separation, identification and relative quantification of membrane proteins,
following their selective covalent modification with a cleavable biotin derivative.
After cell lysis, biotinylated proteins are purified on streptavidin-coated resin and
proteolytically digested. The resulting peptides are analyzed by high-pressure liquid
chromatography (HPLC) and mass spectrometry, thus yielding a two-dimensional
peptide map (2D peptide map). Matrix assisted laser desorption ionization – time of
flight (MALDI-TOF) signal intensity of peptides, in the presence of internal
standards, is used to quantify the relative abundance of membrane proteins from cells
treated in different experimental conditions.
As experimental examples, we present i) an analysis of a BSA-spiked human
embryonic kidney (HEK) membrane protein extract, and ii) an analysis of membrane
proteins of HUVEC cultured in normoxic and hypoxic conditions. This last study
allowed the recovery of the VE-cadherin/actin/catenin complex, revealing an
increased accumulation of beta-catenin at 2% O2 concentration.
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Finally, I will describe a novel methodology, based on the terminal perfusion of
rodents with a reactive ester derivative of biotin, which enables the covalent
modification of proteins that are readily accessible from the bloodstream. Biotinylated
proteins from total organ extracts can be purified on streptavidin resin in the presence
of strong detergents, digested on-resin and submitted to a liquid chromatography -
tandem mass spectrometric analysis for identification.
This in vivo biotinylation procedure led to the identification of hundreds of proteins in
different organs, including proteins that exhibit a restricted pattern of expression in
certain body tissues. Furthermore, the biotinylation of mice bearing F9 subcutaneous
tumors or orthotopic kidney tumors revealed both quantitative and qualitative
differences in the recovery of biotinylated proteins compared to normal tissues,
allowing the identification of accessible markers that are preferentially expressed in
solid tumors.
The technologies described in this thesis open a number of proteomic investigations
on the differential expression of proteins in physiological and pathological processes
in animal models, and should be applicable to human surgical specimens using ex
vivo perfusion procedures.
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2. Introduction
2.1. Antibody-based tumor targeting
Cancer chemotherapy relies on the expectation that anti-cancer drugs will
preferentially kill rapidly dividing tumor cells, rather than normal cells. Since a large
portion of the tumor cells has to be killed in order to obtain and maintain a complete
remission, large doses of drugs are typically used, with significant toxicity towards
proliferating non-malignant cells. Most chemotherapeutic agents do not preferentially
accumulate at the tumor site. Indeed, the dose of drug that reaches the tumor
(normalized per gram of tissue) may be as little as 5-10% of the dose that accumulates
in normal organs! (Bosslet, Straub et al. 1998). The high interstitial pressure and the
irregular vasculature of the tumor account, in part, for the difficult uptake of drugs by
tumor cells. On top of that, the activity of multidrug resistance proteins may further
decrease drug uptake. Indeed, the majority of pharmacological approaches for the
treatment of solid tumors suffer from poor selectivity, thus limiting dose escalation.
The development of more selective anti-cancer drugs, with better discrimination
between tumor and normal cells, is therefore possibly the most important goal of
modern anticancer research.
At present, monoclonal antibodies are the only general class of specific binding
molecules, which can be generated against virtually any antigen. Because of their
binding affinity and long circulation time in blood, they are ideally suited to inhibit
membrane proteins and extracellular proteins, some of which may be relevant for
tumor growth and progression. Indeed, 20 different monoclonal antibodies have
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already been approved by the US Food and Drug Administration for therapeutic
applications, 8 of which for cancer treatment [Rituxan (James and Dubs 1997),
Herceptin (Goldenberg 1999), Mylotarg (Sorokin 2000), Erbitux (Mitchell 2004),
Campath (Ferrajoli, O'Brien et al. 2001), Zevalin (Grillo-Lopez 2002), Bexxar
(Friedberg and Fisher 2004), Avastin (Mitchell 2004)].
Modern Research and Development activities in the field of therapeutic antibodies
clearly focus on the use of fully human monoclonal antibodies, which have a minimal
immunogenicity in patients, unlike rodent antibodies derived from hybridoma
technology. In the past, our laboratory has developed antibody phage libraries (Viti,
Nilsson et al. 2000; Silacci, Brack et al. 2005) and is now fully proficient in the
production of human antibodies directed against virtually any possible antigen.
Anticancer antibodies have been developed, which are in clinical trials in Italy and in
Switzerland (Santimaria, Moscatelli et al. 2003).
While the production of good-quality human monoclonal antibodies is by now an
established experimental methodology in laboratories which have large antibody
libraries and which are conversant with affinity-maturation procedures, the
identification of suitable antigens whose inhibition provides a therapeutic benefit for
the patient remains an essential and important component in the process of developing
anti-cancer antibodies. In turn, only the development and clinical testing of suitable
monoclonal antibodies conclusively demonstrates whether a molecular target is
crucially important for cancer progression, or whether its biological function is
redundant. In addition, monoclonal antibodies may exploit antibody-dependent cell
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cytotoxicity (ADCC) and complement activation mechanisms to display part of their
therapeutic activity.
A detailed description of the tumor environment and an understanding of the
mechanisms underlying tumor progression are at the basis of the quest for new
molecular targets for therapeutic intervention in cancer.
2.2. The tumor environment and tumor-associated antigens
Many tumors in humans can persist in situ for months or even years, surviving as
asymptomatic lesions which are rarely larger than 2 mm3 (Folkman 1971; Folkman
1972; Blood and Zetter 1990). In these early stages of tumor growth, the high rate of
cell proliferation is balanced by a high rate of tumor cell death, probably caused by
the low level of blood perfusion. When cell masses grow, they can change their
microenvironment by a number of ways. They can alter local pH (Vaupel, Okunieff et
al. 1989) and the concentration of nutrients and metabolites, increase interstitial
pressure as a consequence of an expanding cellular volume and increased vascular
permeability, and induce hypoxia by increasing local oxygen consumption (Giordano
and Johnson 2001). This latter phenomenon is particularly important for certain types
of tumor cells that are metabolically very active. Furthermore, the oxygen diffusion
gradient is limited to a range of 100-120 microns around the capillaries. An increase
of the distance to the nearest capillaries determines a rapid reduction of oxygen
availability to the center of the expanding tumor cell mass. This leads to a state of
general hypoxia in the tumor microenvironment that, in turn, provokes the necrosis of
the inner portions of the solid mass. Even though tumors are able to adapt their
metabolism to survive under conditions of reduced oxygen availability by increasing
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glycolysis to maintain ATP production (Semenza 2002), they definitely depend on
adequate oxygen delivery for survival and growth.
It is normally at this time point that a group of tumor cells may switch to the so-called
“angiogenic” phenotype (see also below), by altering its balanced production of
growth factors, cytokines, chemokines and, in particular, of inhibitors and stimulators
of angiogenesis. Angiogenesis initiates with vasodilatation, a process involving nitric
oxide. Vascular permeability increases in response to vascular endothelial growth
factor (VEGF), thereby allowing extravasation of plasma proteins that lay down a
provisional scaffold for migrating endothelial cells (EC). As a next step, EC need to
loosen interendothelial cell contacts in order to migrate from their resident side.
Extracellular matrix (ECM) molecules are degraded by proteinases of the
plasminogen activator, matrix metalloproteinase, chymase and heparanase family,
which also activates or liberates growth factors [basic fibroblast growth factor
(bFGF), VEGF and insuline like growth factor-1] sequestered within the ECM
(Coussens, Raymond et al. 1999). Proteinases also alter the composition of the ECM,
they expose new cryptic epitopes in ECM proteins (such as in collagen IV) or change
their structure (fibrillar versus monomer collagen), which induces EC migration
(Hangai, Kitaya et al. 2002). Once sufficient ECM degradation has taken place,
proliferating ECs migrate and assemble as solid cords that subsequently acquire a
lumen. The establishment of a functional vascular network further requires that
nascent vessels mature into durable vessels. The association of pericytes and smooth
muscle cells with newly formed vessels regulates EC proliferation, survival,
migration, differentiation, vascular branching, blood flow and vascular permeability
(Jain 2003).
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As a consequence, the tumor mass can expand and overtake the rate of internal
apoptosis. At this stage, the majority of tumors become clinically detectable and
capable to invade the surrounding tissues and to metastasize (Blood and Zetter 1990;
Folkman 1995; Hanahan 1998). This so called angiogenic switch (Hanahan 1998)
serves therefore the development of malignant tumors at multiple stages and is
triggered by changes in the local microenvironment of a tumor.
Most efforts in the development of tumor targeting agents have focused on the
targeting of markers located on the membrane of tumor cells. For instance, the
identification of human tumor antigens using various molecular biological and
immunological techniques has enabled the development of immunotherapy, alongside
with that of immune intervention techniques, based on a better understanding of the
mechanisms underlying immunological tumor rejection (Kawakami, Fujita et al.
2004). However, the results so far obtained from reported clinical trials have not been
satisfactory enough for immunotherapy to become one of the standard cancer
therapies. The identified tumor antigens are still limited to a few cancer types, the role
of the identified antigens in in vivo tumor rejection has not been clarified, detailed
mechanisms for some of the tumor escape pathways remain unclear, and interventions
to overcome even the identified problems are currently inefficient. Moreover,
strategies aimed at the direct killing of individual tumor cells can be difficult, because
distant cells may be hardly accessible to ligands and the intrinsic genetic instability of
cancer cells may result in heterogeneous patterns of tumor marker expression.
Consequently, markers that are selectively expressed around tumor blood vessels and
in the tumor stroma may offer a number of potential advantages, such as better
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accessibility, stability and abundance. Furthermore, agents conceived to target
molecules present in or around the tumor vasculature and impair its functions might
provoke extensive damage to thousands of tumor cells simply by preventing them
from receiving nutrients and oxygen from the circulation.
Until now, the search for tumor-associated vascular antigens has mainly relied on
serendipitous discovery (Zardi, Carnemolla et al. 1987; Liu, Moy et al. 1997;
Carnemolla, Castellani et al. 1999), on immunization strategies (Buhring, Muller et al.
1991), on transcriptomic analysis of tumor-derived endothelial cells (St. Croix, Rago
et al. 2000; Wyder, Vitaliti et al. 2000) and on bioinformatics approaches (Gerritsen,
Soriano et al. 2002; Huminiecki, Gorn et al. 2002). Furthermore, the groups of
Ruoslahti and Pasqualini have pursued the in vivo panning of peptide phage libraries
for the discovery of “signature peptides” capable of identifying vascular structures in
normal organs and in tumors (Pasqualini and Ruoslahti 1996).
2.3. Angiogenesis and tumor angiogenesis
Angiogenesis, defined as the development of new blood vessels from preexisting
vessels, is one out of several mechanisms that build and maintain the blood supply of
the body’s tissues. As such, it can be distinguished from arteriogenesis and
vasculogenesis. Arteriogenesis is a repair mechanism whereby bridging collateral
arterioles are remodeled and grow to compensate for arterial occlusions in major
vessels (Schaper and Buschmann 1999; Carmeliet 2000). Vasculogenesis, on the other
hand, is involved in the initial steps of the formation of the vascular system during
embryogenesis. In this process, mesodermal cells differentiate into angioblasts which
then give rise to the endothelial cells assembling into a first vascular network
(Folkman 1995; Risau and Flamme 1995). Because vasculogenesis only leads to an
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immature, poorly functional vasculature in the embryo, angiogenesis is essential for
the subsequent development of the vascular network of arteries, veins, arterioles,
venules and capillary blood vessels (Folkman 1995; Bischoff 1997; Risau 1997).
Angiogenesis is a physiological process in embryogenesis and during development. In
the adult, angiogenesis is a prominent feature of the female reproductive cycle.
Moreover, a high rate of endothelial cell turnover is observed in the testis (Collin and
Bergh 1996; LeCouter, Lin et al. 2003). Hair growth is also associated with
pronounced vascular endothelial growth factor (VEGF)-induced angiogenesis (Chase
1954; Hardy 1992; Yano, Brown et al. 2001; Yano, Brown et al. 2003). Otherwise,
while angiogenesis is essentially a rare event in the adult (with the notable exceptions
indicated above), it can occur in a number of relevant pathologies, such as cancer,
blinding ocular disorders, rheumatoid arthritis, psoriasis (Folkman 1995; Carmeliet
and Jain 2000).
The observation by Tannock (Tannock 1968) in 1968 that the vasculature is in rapid
proliferation within the tumor was followed few years later by articles of Folkman
(Folkman 1971), who postulated that the growth of new blood vessels is an essential
requirement for tumors to grow beyond a certain size. As a consequence, inhibition of
angiogenesis would represent an avenue for blocking tumor growth (Gullino 1981),
possibly circumventing the multidrug resistance problem, since the endothelial cells
which line tumor blood vessels are genetically stable, unlike the tumor cells (Kerbel
1991). The causal link between tumor hypoxia and induction of angiogenesis
(Richard, Berra et al. 1999), as well as the molecular machinery for the sensing of and
response to hypoxia, are by now well characterized (Maxwell, Dachs et al. 1997;
Carmeliet, Dor et al. 1998; Ratcliffe, O'Rourke et al. 1998).
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Mammalian cells are able to sense prolonged decreases in oxygen tension through a
conserved hypoxic response pathway, which aims at increasing the local oxygen
concentration by several actions. An important mediator in this process is the HIF
complex, which increases the transcription of a broad range of genes including
angiogenic growth factors like VEGF, but also erythropoietin, glucose transporters
and glycolytic enzymes (Semenza 1999). Furthermore, hypoxia promotes the
stabilization of the VEGF mRNA, which is rapidly degraded under normoxia
(Damert, Machein et al. 1997; Dibbens, Miller et al. 1999). Those two events
contribute to VEGF up-regulation.
VEGF specifically stimulates the growth of endothelial cells, which in turn produce
many other nonspecific angiogenic stimulators, including bFGF, acid fibroblast
growth factor (aFGF), transforming growth factor-α (TGF-α), transforming growth
factor-β (TGF-β) and platelet derived endothelial cell growth factor (PD-ECGF). In
addition, tumor cells and endothelial cells excrete proteolytic enzymes, such as matrix
metalloproteinases (MMPs) and serine proteases (e.g. tissue plasminogen activator
and urokinase plasminogen activator) which break down the ECM. Cell adhesion
molecules, such as integrins (αvβ3 and αvβ5) are expressed on the surface of
endothelial cells and mediate interactions with the ECM. Laminin, tenascin and type
IV collagen are also produced to provide new basement membrane components. In
addition, expression of VEGF receptors, members of the ephrin receptors family
(Easty, Hill et al. 1999; Holder and Klein 1999; Helbling, Saulnier et al. 2000) and the
Tie-1 and Tie-2 receptors (McCarthy, Crowther et al. 1998; Siemeister, Schirner et al.
1999) are up regulated. The latter interact with angiopoietins to signal capillary
organization. Furthermore, the induction of cytokines and chemokines also recruits
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monocytes and leukocytes, producing local inflammatory reactions that aid in the
process.
Up-regulation of angiogenic factors, however, is not sufficient in itself for a tumor to
become angiogenic: negative regulators of vessel growth have to be down-regulated
(Folkman 1995).
Thrombospondin was the first protein for which a down regulation during
tumorigenesis was demonstrated (Tenan, Fulci et al. 2000). A number of endogenous
angiogenesis inhibitors have been identified since. Some of them are cryptic regions
or fragments of proteins, which in their intact form do not possess any anti-angiogenic
activity. Examples include a fragment of platelet factor 4 (Gupta, Hassel et al. 1995),
or an anti-thrombin III fragment (O'Reilly, Pirie-Shepherd et al. 1999), the precursor
proteins of both being members of the clotting/fibrinolytic pathways. Often, however,
the intact proteins represent components of the extracellular matrix, as it is the case
for angiostatin (plasminogen) (O'Reilly, Holmgren et al. 1994), endostatin (collagen
XVIII) (O'Reilly, Boehm et al. 1997) and PEX (matrix metalloproteinase 2) (Brooks,
Silletti et al. 1998).
Studies attempting to correlate tumor prognosis with vessel counts have often led to
the misleading concept that tumor vascularity and tumor angiogenesis are
synonymous (Eberhard, Kahlert et al. 2000). Indeed, several examples are known of
highly vascular lesions which are benign (Castellani, Viale et al. 1994; Castellani,
Borsi et al. 2002). However, careful studies with double staining techniques (detecting
proliferating endothelial cells, or blood vessels which stain with certain markers of
angiogenesis) have shown that exuberant tumor angiogenesis remains the most
important parameter associated with poor prognosis (Eberhard, Kahlert et al. 2000;
Castellani, Borsi et al. 2002).
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Recently, it has been suggested that tumor blood vessels may have a "mosaic"
structure, with tumor cells lining the blood vessels wall instead of endothelial cells
(Maniotis, Folberg et al. 1999; Chang, di Tomaso et al. 2000; Folkman 2001), but
some authors question the relevance of these findings (Barinaga 1999; Bissell 1999;
McDonald, Munn et al. 2000).
As mentioned above, the development of metastases is also dependent on
angiogenesis (Folkman 1995). First, metastatic cells are not shed from a primary
tumor until the tumor has become vascularized. Second, once metastatic cells have
colonized a target organ, they will again only grow to a metastasis of clinically
detectable size if they can induce neovascularization. Furthermore,
lymphangiogenesis (i.e., the proliferation of new lymphatic blood vessels) is
emerging as a biological process which may facilitate tumor metastatic spread
(Mandriota, Jussila et al. 2001; Padera, Kadambi et al. 2002; Stacker, Achen et al.
2002; Rafii and Skobe 2003).
Tumor blood vessels are architecturally different from their normal counterparts –
they are irregularly shaped, dilated, tortuous and can have dead ends (Denekamp
1982). During physiological angiogenesis, new blood vessels rapidly mature and
become stable, while tumor blood vessels fail to become quiescent. Consequently, the
tumor vasculature develops unique characteristics and becomes distinct from the
normal blood supply system. Characteristic molecular species (i.e., proteins) which
are more abundant in tumoral blood vessels than in normal tissues may serve as
markers for angiogenesis. The identification of such molecular markers has extended
the field of anti-angiogenic-therapy. The new field of vascular targeting features the
17
use of molecular vehicles, which selectively localize in the neovasculature and which
allow the targeted delivery of therapeutic agents.
2.4. Markers of angiogenesis
To date, only few good-quality markers of angiogenesis, located either on endothelial
cells or in the modified sub-endothelial extracellular matrix, have been described and
sufficiently characterized. The biggest problem with most of the markers described so
far is their non-negligible expression in normal tissues, which may negatively affect
imaging and therapeutic applications of ligands specific for these markers.
Systematic ex vivo investigations of tumor endothelial structures using proteomic
techniques (Schnitzer, Oh et al. 1995; Schnitzer 1998), biopanning of phage display
libraries (Rousch, Lutgerink et al. 1998; Hoogenboom, Lutgerink et al. 1999;
Ruoslahti 2000; Kolonin, Pasqualini et al. 2001) or transcriptomic techniques, such as
serial analysis of gene expression (St. Croix, Rago et al. 2000; Wyder, Vitaliti et al.
2000; Carson-Walter, Watkins et al. 2001), are revealing new candidate tumor
endothelial markers. Their validation, however, requires the generation of specific
monoclonal antibodies, a comprehensive immunohistochemical analysis, and
quantitative biodistribution studies in animal models of angiogenesis-related diseases.
In the following pages, some of the most prominent markers of angiogenesis currently
known will be briefly presented.
2.4.1. EDB domain of fibronectin
The EDB domain of fibronectin is one of the best markers of angiogenesis known so
far. In the adult, this small domain of 91 amino acids (discovered in 1987 by Zardi et
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al. (Zardi, Carnemolla et al. 1987) at the National Institute for Cancer Research,
Genoa, Italy) is usually absent in both plasma- and tissue-fibronectin (except for some
blood vessels of the ovaries and of the endometrium in the proliferative phase).
However, it may become inserted in the fibronectin molecule by a mechanism of
alternative splicing at the level of the primary transcript during active tissue
remodeling (including angiogenesis), giving rise to a prominent perivascular pattern.
The EDB domain is identical in sequence in mouse, rat, rabbit, dog, monkey and man.
Probably because of tolerance, the generation of monoclonal antibodies to the EDB
domain has not been possible so far using hybridoma technology (Peters, Trevithick
et al. 1995). However, using human antibody libraries, we have generated a number
of good-quality anti-EDB antibodies (Carnemolla, Neri et al. 1996; Pini, Viti et al.
1998; Giovannoni, Viti et al. 2001). In particular, the human antibody fragment L19
in single-chain Fv antibody fragment configuration, " scFv" (Huston, Levinson et al.
1988), has been shown to target both tumor and non-tumor angiogenesis in animal
models (Birchler, Viti et al. 1999; Tarli, Balza et al. 1999; Viti, Tarli et al. 1999;
Demartis, Tarli et al. 2001) and in patients with cancer (Santimaria, Moscatelli et al.
2003).
Another antibody often used to stain "oncofetal fibronectin" is MAb FDC-6 of
Matsuura and Hakomori (Matsuura and Hakomori 1985), which is, however, directed
against the IIICS region of the FN molecule.
2.4.2. Integrins αvβ3 and αvβ5
Some integrins, in particular αvβ3 and αvβ5, have been proposed both as markers for
ligand-based targeting strategies and as functional mediators of angiogenesis in
tumors and in ocular disorders (Brooks, Montgomery et al. 1994; Friedlander, Brooks
19
et al. 1995; Friedlander, Theesfeld et al. 1996; Sipkins, Cheresh et al. 1998).
Immunohistochemistry studies have shown that a number of normal tissues stain
positive for the antigen, though to a lower extent compared to tissues undergoing
active angiogenesis (Max, Gerritsen et al. 1997). A Phase I immunohistoscintigraphy
clinical trial in 20 patients with cancer, using the radio labeled humanized antibody
Vitaxin, failed to image the tumor lesions in all but one patient (Posey, Khazaeli et al.
2001).
RGD-containing peptides, capable of high affinity binding to αvβ3 integrin, have
been used successfully for the radio imaging of tumor in animal models (Haubner,
Wester et al. 2001; Haubner, Wester et al. 2001; Posey, Khazaeli et al. 2001). While
good tumor/blood ratios were observed at early time points (1–2 h), tumor/organ
ratios were sometimes poor (particularly colon, kidney, liver, and lung).
2.4.3. Prostate-specific membrane antigen (PSMA)
This enzyme has originally been used as a serum marker for prostate cancer,
providing a clinical prognostic information complementary to the one of other
markers (Murphy, Kenny et al. 1998; Murphy, Su et al. 2000; Holmes 2001). A
number of reports have indicated a strong expression of PSMA around blood vessels
in a wide variety of carcinomas (Liu, Moy et al. 1997; Chang, O'Keefe et al. 1999).
PSMA expression in blood vessels was also reported. An immunoscintigraphy clinical
trial with radio labeled de-Immunized MAb J591-DOTA-111In is in progress at
Cornell University.
20
2.4.4. Endoglin (CD105)
The initial excitement about the potential of endoglin as a marker of angiogenesis
(Wang, Kumar et al. 1993) has been slowed down by later reports of significant
expression of the antigen in a number of normal organs (Burrows, Derbyshire et al.
1995; Balza, Castellani et al. 2001). While most of the quantitative biodistribution
results obtained with radio labeled anti-endoglin antibodies in tumor-bearing mice
were rather poor (Bredow, Lewin et al. 2000), good imaging results in tumor-bearing
dogs have also been reported (Fonsatti, Jekunen et al. 2000).
2.4.5. VEGF and VEGF–receptor complex
VEGF (available in different forms) is one of the main mediators of the
vascularization of solid tumors. In the tumor microenvironment, an up-regulation of
both VEGF and its receptors occurs, leading to a high concentration of occupied
receptors on tumor vascular endothelium. VEGF–receptor complexes were shown to
be a specific target on tumor endothelium for antibodies in vivo. In a recent study, a
monoclonal antibody (2C3) was shown to have anti-tumor activity against tumor
xenografts in mice (Brekken, Overholser et al. 2000). This antibody has also been
shown to localize to tumor blood vessels by microscopic analysis, but quantitative
biodistribution results have not yet been reported. Biodistribution studies in tumor-
bearing mice with anti-VEGF antibodies or with VEGF itself as a ligand for its
receptors have been disappointing (Cooke, Boxer et al. 2001; Halin, Niesner et al.
2002). It is worth mentioning that a humanized neutralizing antibody to VEGF
(Avastin, Genentech) is currently in Phase III clinical trials (Ferrara 2002).
21
2.4.6. CD44
CD44 is a cell adhesion receptor of great molecular heterogeneity due to alternative
splicing and posttranslational modifications. In spite of its widespread pattern of
expression in blood cells and tissues, a monoclonal antibody to a CD44 variant has
been reported to display spectacular tumor targeting results in tumor-bearing mice,
with prominent perivascular accumulation (Wakai, Matsui et al. 2000). At this time
point, it is not clear if the excellent targeting results (with % injected dose in tumor
>75%, 1 h after injection in tumor-bearing mice) are due to a predominantly luminal
pattern of expression of the antigen (Tsunoda, Ohizumi et al. 1999).
2.4.7. Phosphatidyl serine phospholipids
Phosphatidylserine phospholipids are normally located in the inner leaflet of the cell
membrane and, therefore, not readily accessible to specific ligands. However, in cells
undergoing apoptosis or under stress, they may become exposed in the outer cell
membrane leaflet (Zachowski, Henry et al. 1989). Recently, Thorpe and colleagues
have postulated that phosphatidylserine may serve as marker of angiogenesis for
ligand-based vascular targeting applications, on the basis of binding studies with
Annexin V and monoclonal antibodies on endothelial cells undergoing oxidative
stress. This hypothesis is supported by a fluorescence microscopic analysis of tumor
targeting experiments with monoclonal antibodies injected in tumor bearing mice
(Ran, Downes et al. 2002). The real potential of phosphatidylserine for vascular
targeting applications remains to be confirmed by quantitative biodistribution studies.
A potential concern comes from the surface exposure of phosphatidylserine in
activated platelets (Bucki, Janmey et al. 2001; Monroe, Hoffman et al. 2002).
22
2.4.8. Large isoforms of tenascin C
Tenascin C is a component of the extracellular matrix, which exists in a "small"
isoform (devoid of extra-domains) or in a more tissue-restricted "large isoform",
which contains additional domains inserted by alternative splicing. Although
expression of the large isoform of tenascin-C is detectable in certain normal tissues
(e.g., at the interface between derma and epidermis), the protein is much more
abundant in several aggressive tumors, with a prominent staining of the tumor stroma
and of the tumor neo-vasculature (Borsi, Carnemolla et al. 1992). Radio labeled
monoclonal antibodies specific for the large tenascin isoform have been investigated
in the clinic for several years, both in diagnostic and radioimmunotherapeutic
applications (Riva, Arista et al. 1992; Bigner, Brown et al. 1998; Paganelli, Grana et
al. 1999; Paganelli, Bartolomei et al. 2001). Recently, it has been discovered that the
extra-domain C within the large isoform features an even more restricted pattern of
expression, being undetectable in normal human tissues, but expressed in aggressive
tumors such as high-grade astrocytomas and lung cancers (Carnemolla, Castellani et
al. 1999), with a predominantly vascular staining pattern.
2.4.9. Magic roundabout
Magic roundabout (MR or ROBO4) belongs to the roundabout family, which contains
several closely related genes (three in humans) that were previously thought to be
only present in neuronal tissue and involved in axon guidance. Roundabouts have five
IgG and three fibronectin-like extracellular domains. They are large transmembrane
receptors for ligands known as slits. The discovery of an endothelial specific
roundabout has been demonstrated by a combination of Northern blotting, in situ
hybridization and immunohistochemistry, confirming a highly restricted pattern of
23
expression (Huminiecki, Gorn et al. 2002). MR is highly expressed during embryonic
development, but is absent from adult tissues except at sites of active angiogenesis,
including tumors. A similar pattern of expression has also been found for delta4, an
endothelial specific member of the delta family (Mailhos, Modlich et al. 2001).
2.4.10. Components of the coagulation cascade
Although components of the blood clotting cascade cannot be considered markers of
angiogenesis in the strict sense, the interaction between malignant cell growth and
coagulation disorders has been recognized (Prandoni, Piccioli et al. 1999; Donati and
Falanga 2001; Gale and Gordon 2001; Rickles and Falanga 2001). Experimental data
suggest that coagulation and fibrinolysis play a significant role in growth, invasion,
dissemination and metastasis formation (Prandoni, Piccioli et al. 1999; Donati and
Falanga 2001; Gale and Gordon 2001; Rickles and Falanga 2001; Francis and
Amirkhosravi 2002). Proteomic studies have identified components of the coagulation
cascade which were specifically up regulated in cancer tissues (Wang, Wang et al.
2004). Moreover, results from a number of clinical studies indicate that various
molecules of the coagulation and fibrinolysis system participate in the growth and
dissemination of tumor cells (Nielsen, Pappot et al. 1998; Brunner, Nielsen et al.
1999; Korte 2000; Gale and Gordon 2001; Rickles and Falanga 2001). It has recently
become clear that the primary initiator of coagulation, tissue factor (TF), is expressed
in a variety of solid tumors and tumor cell lines (Hamada, Kuratsu et al. 1996; Vrana,
Stang et al. 1996; Koomagi and Volm 1998; Lwaleed, Chisholm et al. 1999;
Abdulkadir, Carvalhal et al. 2000; Gale and Gordon 2001; Ueda, Hirohata et al.
2001). Subsequent results have suggested that TF participate in the growth and
dissemination of various cancer types. Thus, in prostate carcinoma TF expression
appears to correlate significantly with tumor angiogenesis, determined by micro
24
vessel density (MVD) and with pre-operative PSA level (Abdulkadir, Carvalhal et al.
2000). In nonsmall-cell lung carcinoma (NSCLC), TF expression has been shown to
be associated with MVD and with the expression of VEGF (Koomagi and Volm
1998). TF may also play a certain role in breast cancer, where a correlation between
progression to invasive cancer and the TF expression by adjacent stroma cells has
been shown (Vrana, Stang et al. 1996). Results from experimental and clinical studies
have suggested that TF may play a prominent role in growth and dissemination of
colorectal cancer (Scarlett, Thurlow et al. 1987). Thus, TF expression on tumor cells
appears to be related to stage of disease; the higher the stage, the higher TF expression
(Nakasaki, Wada et al. 2002). In addition, high TF expression on tumor cells may be
related to subsequent development of hepatic metastasis (Shigemori, Wada et al.
1998).
Both fibrinogen and fibrin have been localized to the tumor–host cell interface
(Brown, Van de Water et al. 1988; Costantini, Zacharski et al. 1991). Fibrin is
abundant in different types of tumors (Clark, Lanigan et al. 1982; Dvorak, Senger et
al. 1983; Dvorak, Harvey et al. 1987; Nagy, Brown et al. 1989; Dvorak, Nagy et al.
1992) such as primary brain lesions (Bardos, Molnar et al. 1996) and prostate cancer
(Wojtukiewicz, Zacharski et al. 1991). Tumor cell associated fibrin deposition is also
found in small cell carcinoma of the lung, renal carcinoma and malignant melanoma
in association with activated coagulation factors such as Factor XIIIa or Xa, indicative
of thrombin generation at the primary tumor site. Positive fibrin staining is frequently
found in focal sites at the interface of the tumor cells and surrounding stroma, but not
in the ECM of normal host cells. In addition, abundant deposition of fibrinogen is
found predominantly within the extracellular tumor stroma. In contrast, using
immunohistochemical staining with monoclonal antibodies 18C6 and T2G1,
25
Costantini and colleagues (Costantini, Zacharski et al. 1991) reported that abundant
fibrinogen, but not fibrin, is present within the tissue stroma in breast cancer, although
fibrin deposition has been localized to the tumor-normal tissue interface (Dvorak,
Dickersin et al. 1981). In addition, fibrinogen, but not fibrin, deposition is a feature of
mesothelioma (Wojtukiewicz, Zacharski et al. 1989), colon cancer (Wojtukiewicz,
Zacharski et al. 1989), and lymphoma (Costantini, Zacharski et al. 1992). Fibrin
surrounding tumors may protect them from infiltrating inflammatory cells by acting
as a barrier, thus preventing inflammatory reactions directed towards the tumor cells
(Dvorak, Dickersin et al. 1981; Dvorak, Senger et al. 1983)
2.5. Transcriptomic methods for target identification
2.5.1. Evolution of transcriptomics
A number of technologies have been developed for gene expression profiling in cells
or tissues at the level of the mRNA transcripts. The oldest and simplest procedure to
determine whether a gene is expressed in a sample is the Northern blot (Alwine,
Kemp et al. 1977). Northern blot analysis involves the transfer of electrophoretically
separated RNA molecules from an agarose gel to nitrocellulose membranes. Specific
RNA molecules can be detected by hybridization with 32P-labeled DNA or RNA
probes followed by autoradiography (Melton, Krieg et al. 1984). A more sensitive
method than Northern blotting is the RNase protection assay, the basis of which is a
solution hybridization of a single-stranded, discrete-sized 32P-labeled anti-sense
probe(s) to an RNA sample. Non-hybridizing (single-stranded) RNA species are
digested with RNase followed by the extraction and electrophoretic isolation of
hybridized fragments. To quantify mRNA levels, the intensities of hybridized probe
26
fragments are compared with the intensities generated from either an endogenous
internal control (relative quantization) or known amounts of sense-strand RNA
(Prediger 2001). Like Northern blotting, this method is hampered by a low throughput
and by a low number of genes, which can be analyzed in parallel.
A widely used technique allowing the identification of many differentially regulated
genes at once is the subtractive hybridization. In the course of subtractive
hybridization, an excess of one population of cDNA (driver) is labeled with biotin
prior to the hybridization with another population of cDNA (tracer). Biotinylated
driver/tracer hybrids and unhybridised driver cDNA species are removed by
streptavidin precipitation. The subtracted product is used for two or three further
rounds of subtraction yielding a population of cDNA species enriched for sequences
preferentially expressed in the tracer. The isolated fragments are amplified, cloned
and analyzed (Byers, Hoyland et al. 2000). Implementing the polymerase chain
reaction (PCR) yielded higher sensitivity to the method. However, subtractive
hybridization is not suitable for quantitative measurements of gene expression.
Very often, differential gene expression is investigated by differential display-PCR
(DD-PCR), since it is easy to apply, cheap and relies on PCR (Liang and Pardee
1992). Differential display is based on a series of steps: the isolation of undegraded
cellular RNA; reverse transcription of the RNA with an “anchored” oligo-dN1-2 T12-18
to produce a subset of single-stranded cDNAs; the PCR amplification with the same
and/or another oligo to enrich a subset of cDNAs; and, ultimately, display on a
polyacrylamide gel, where differences between two different pools of RNA can be
visualized, and from which specific differentially displayed fragments can be excised
and sequenced. DD-PCR has proven to be a very powerful tool to isolate new genes
27
and investigate differential gene expression, but it also generates a lot of false-
positives (Diatchenko, Lau et al. 1996).
Quantitative PCR is becoming more and more the preferred choice to prove
differences in expression of known genes, but, in contrast to DD-PCR, it does not
allow the identification of unknown genes (O'Garra and Vieira 1992; Freeman 1998);
however, it is more sensitive than Northern blotting and the RNase protection, and
one can establish differences in expression levels of genes of which only a few
mRNAs are present per cell. The relative amount of PCR products (relative to an
internal control) can be compared. Currently, fluorescence-based kinetic (real-time)
PCR allows the monitoring of amplification during a PCR, and more accurately
reflects the relative expression levels of target genes (Bustin 2000). In general, the
methods described above feature low throughput and the gene expression analysis is
semi-quantitative at best.
High-throughput, cost-effective technologies have evolved capable of simultaneously
quantifying tens of thousands of defined mRNA species in a miniaturized, automated
format.
The series analysis of gene expression (SAGE) allows the serial analysis of short
cDNA sequences or ‘tags’ derived from a defined position within a cDNA, and allows
both qualitative and quantitative analysis (Velculescu, Zhang et al. 1995). SAGE is
based on the serial sequencing of 15-bp tags that are unique to each and every gene.
These gene-specific tags are produced by a series of molecular biological
manipulations and then concatenated for automated sequencing. For model
organisms, the gene corresponding to each tag can be identified immediately and
unambiguously. A list of each unique tag and its abundance in the population is
28
assembled. A great advantage of SAGE is that the method is unbiased by
experimental conditions, so direct comparison of data sets is possible.
The use of cDNA and oligonucleotide arrays, on either filters or microslides (‘chips’),
is becoming the standard in large-scale differential gene expression analysis.
Microarrays do not require extensive sequencing, as opposed to SAGE and therefore
represent a truly high throughput method.
In the technology of cDNA microarrays, PCR-amplified cDNA fragments (ESTs) are
spotted at high density (10 – 50 spots per mm2) onto a microscope slide and probed
against fluorescently or radioactively labeled cDNA (Schena, Shalon et al. 1995). The
intensity of signal observed is assumed to be in proportion to the amount of transcript
present in the RNA population being studied. Differences in intensity reflect
differences in transcript level between treatments. Statistical and bioinformatics
analyses are then performed, usually with the goal of generating hypotheses that may
be tested with established molecular biological approaches (Brown and Botstein
1999).
The second general approach to parallel analysis of gene expression is the use of
oligonucleotide microarrays, also known by the trademark Affymetrix GeneChip®
(Lockhart, Dong et al. 1996; Lipshutz, Fodor et al. 1999). The manufacture of
GeneChip arrays uses photolithography and solid-phase chemistry to produce arrays
containing hundreds of thousands of 25-mer oligonucleotide probes packed at
extremely high densities. The probes are designed to maximize sensitivity, specificity,
and reproducibility, allowing consistent discrimination between specific and
background signals, and between closely related target sequences. In a standard
eukaryotic gene expression assay, labeled cDNA or cRNA targets derived from the
mRNA of an experimental sample are hybridized to nucleic acid probes attached to
29
the solid support. By monitoring the amount of label associated with each DNA
location, it is possible to infer the abundance of each mRNA species represented. The
combination of the miniaturization of the technology and the large and growing
amounts of sequence information has enormously expanded the scale at which gene
expression can be studied. Nowadays, Affymetrix offers GeneChip arrays allowing
the simultaneous analysis of over 47,000 transcripts (e.g. the Human Genome U133
Plus 2.0 Array).
2.5.2. Application of transcriptomic methods to the identification of new targets for
tumor therapy
A number of studies, performed at a genome-wide transcriptional level, have been
carried out in order to identify potential new targets for therapeutic intervention in
cancer. Many of these studies have tried to outline differences between aggressive and
non-aggressive tumor cell lines. For example, by analyzing highly metastatic
melanoma cells, Hynes and colleagues defined a pattern of gene expression that
correlates with progression to a metastatic phenotype (Clark, Golub et al. 2000).
Among other genes, the small GTPase RhoC that regulates the actin-based
cytoskeleton was shown to be essential in tumor cell invasion. Weidle and colleagues
(Evtimova, Zeillinger et al. 2003) compared by GeneChip technology the
transcriptional profiles of four invasive and four non-invasive mammary carcinoma
cell lines, using normal mammary epithelial cells as a reference. The expression of 18
transmembrane receptors, 18 secreted proteins and 5 kinases was found to be
increased in invasive vs. non-invasive mammary carcinoma cell lines. Besides several
genes already described as prognostic markers or putative targets for treatment of
mammary carcinoma, the authors identified different new genes and indicated in the
30
transmembrane tyrosine kinase DDR2, in the transmembrane receptors PMP22 and
EMP3 and in the adhesion molecule N-cadherin, a panel of possible new markers of
invasiveness in mammary carcinoma.
A constantly increasing wealth of literature has dealt with the comparison, at a
genome-wide transcriptomic level, of specimens from different human tumors and
their respective normal tissue counterpart. Examples include, but are by far not
limited to, human prostate cancer (Wolf, Mousses et al. 2004), colorectal carcinomas
(Bustin and Dorudi 2004; Kim, Nam et al. 2004), rhabdomyosarcoma (Schaaf, Ruijter
et al. 2005), adrenocortical tumors (de Fraipont, El Atifi et al. 2004), endometrial
cancer (Holland, Saidi et al. 2004), and nasopharyngeal carcinoma (Sriuranpong,
Mutirangura et al. 2004). A study, performed by means of SAGE analysis, compared
gene expression among different specimens of normal breast tissue, ductal carcinoma
in situ (DCIS) and invasive ductal carcinoma (Abba, Drake et al. 2004). Fifty-two
genes were found to be deregulated between DCIS and normal breast tissue, 149
between invasive ductal carcinoma and DCIS. Many molecular players involved in
the extracellular matrix remodeling and invasion process were found to be up
regulated in invasive ductal carcinoma, among which matrix metalloproteinase 2,
SPARC and lumican (Abba, Drake et al. 2004).
SAGE technique was also used by St Croix and colleagues to gain a molecular
understanding of tumor angiogenesis (St. Croix, Rago et al. 2000). They compared
gene expression patterns of endothelial cells derived from blood vessels of normal and
malignant colorectal tissues. Of over 170 transcripts predominantly expressed in the
endothelium, 79 were differentially expressed, including 46 that were specifically
elevated in tumor-associated endothelium. Several of these genes encode extracellular
matrix proteins, but most were of unknown function. Most of these tumor endothelial
31
markers were expressed in a wide range of tumor types, as well as in normal vessels
associated with wound healing and corpus luteum formation. These studies
demonstrated that tumor and normal endothelium are distinct at the molecular level, a
finding that may have significant implications for the development of anti-angiogenic
therapies.
Other studies have reported the analysis of gene expression as a response to hypoxia,
using microarray technologies, in a number of different cell types (including T84 and
Caco-2 intestinal epithelial cells (Narravula and Colgan 2001; Lee, Madan et al.
2002), PC12 pheochromocytoma cells (Seta, Kim et al. 2001), HepG2 and HepG3
hepatoma cells (Fink, Ebbesen et al. 2001), RKO lymphoblastoid cells (Hammond,
Denko et al. 2002), A172 glyoblastoma cells (Budanov, Shoshani et al. 2002) and
PC10 pancreatic ductal carcinoma cells (Niizeki, Kobayashi et al. 2002)), as well as in
developing zebrafish embryos (Ton, Stamatiou et al. 2002). Hypoxia-regulated genes
have also been identified by subtractive/differential mRNA analytical techniques
(e.g., subtractive suppression hybridization; (Seta, Kim et al. 2001)) and confirmed by
in situ hybridization (Budanov, Shoshani et al. 2002).
2.6. Proteomic methods for target identification
2.6.1. Evolution of proteomics: gel-based methods
2.6.1.1. Two-dimensional polyacrylamide gel electrophoresis
32
Two-dimensional polyacrylamide gel electrophoresis (2D-PAGE) is a powerful
method for the analysis of complex protein mixtures extracted from cells, tissues or
other biological samples. This technique, which was first introduced by P.H. O’Farrell
(O'Farrell 1975) and J. Klose (Klose 1975), sorts proteins according to two
independent properties in two discrete steps: the first dimension step, isoelectric
focusing (IEF), separates proteins according to their isoelectric points (pI); the
second-dimension step, SDS-polyacrylamide gel electrophoresis (SDS-PAGE),
separates proteins according to their molecular weights (MW). 2D-PAGE is a core
technique in proteome analysis, which usually includes sample preparation, 2D-
PAGE, post separation image analysis of the stained gel and protein characterization
by mass spectrometry. Technical innovations within the last twenty years made the
2D-PAGE a widely applied analytical tool. The replacement of classical first-
dimension carrier ampholyte pH gradients by well-defined immobilized pH gradients
(IPG) resulted in higher resolution, improved interlaboratory reproducibility, higher
protein loading capacity and an extended basic pH limit for 2D-PAGE (Gorg, Postel
et al. 1988). Furthermore, microanalytical techniques were developed, which allowed
the identification of proteins at the amounts available from 2D-PAGE. These
microanalytical techniques were first Edman sequencing (Matsudaira 1987) and, more
recently, mass spectrometry, which has greatly increased the sensitivity and
throughput of the protein identification (Yates, Speicher et al. 1993; James, Quadroni
et al. 1994). Software is available, which allows the routine computerized evaluation
and semi-quantification of the highly complex two-dimensional patterns. Data about
entire genomes (or substantial fractions thereof) are available for a number of
organisms, allowing rapid identification of the gene encoding a protein separated by
2D-PAGE. The World Wide Web provides simple, direct access to spot pattern
33
databases for the comparison of electrophoresis results and to genome sequence
databases for assignment of sequence information.
However, despite all these upgrades, there are still limitations of 2D-PAGE, which are
linked to the chemical diversity of proteins in a cell or tissue and to their different
abundance.
The comparison between the number of actin molecules (ca. 108 molecules per cell)
and the number of some cellular receptors (ca. 100 – 1000 molecules per cell) present
in a cell, reveals a dynamic range of up to six orders of magnitude between the most
abundant and the least abundant proteins (Rabilloud 2002). 2D-PAGE in contrast can
only display differences in protein concentration in the range of 100- to 10´000-fold.
Since membrane proteins are typically little abundant, they are often underrepresented
on 2D-PAGE gels. Furthermore, complex protein mixtures lead to co-migrating
proteins which compromise quantitative analysis, based on the assumption that one
protein is present per spot (Gygi, Corthals et al. 2000). To overcome this problem,
highly expressed proteins can be purified away by sample prefractionation or
enrichment strategies. Membrane proteins can be enriched by cellular fractionation
(Walter and Blobel 1983), but a difficult solubilization during sample preparation for
2D-PAGE often limits the applicability of this technology. Furthermore, membrane
proteins tend to precipitate during IEF, when they concentrate at their pI.
IEF sample buffer for 2D-PAGE using immobilized pH (IPG) gradients usually
contains a chaotropic agent to solubilize and denature proteins, a non-ionic or
zwitterionic detergent to solubilize hydrophobic proteins, and a reducing agent, which
cleaves disulfide bonds to allow a more complete protein unfolding. Additional
components include a carrier ampholyte mixture, which can improve separations and
sample solubility, and a tracking dye. The strong anionic detergent SDS, which is
34
known to solubilize almost any protein, interferes with the isoelectric focusing step in
the first dimension of 2D-PAGE and can only be used in a concentration below 0.25%
(Ames and Nikaido 1976; Harder, Wildgruber et al. 1999). IEF requires the
maintenance of the intrinsic surface charge of proteins. SDS, which binds highly to
proteins, produces a strong charge shift, impairing subsequent IEF. Salts, which help
to solubilize proteins, lead to a horizontal streaking in the 2D-gels and therefore have
to be avoided. Several approaches have been taken to increase the representation of
membrane proteins on 2D-gels. Molloy and colleagues extracted membrane proteins
of a whole cell lysate from Escherichia coli (E.coli) by differential solubilization
(Molloy, Herbert et al. 1998). In a three-step sequential solubilization protocol,
membrane proteins were separated from other cellular proteins by their insolubility in
solutions conventionally used for IEF. Eleven membrane proteins could be identified
in the final membrane-rich pellet. Another approach tempted to isolate membrane
proteins from E.coli by organic solvent extraction prior to 2D-PAGE (Molloy,
Herbert et al. 1999). The use of an organic solvent extraction selectively enriched
hydrophobic proteins, which could be resolubilized in denaturing conditions in order
to allow 2D-PAGE analysis. However, no highly hydrophobic protein typical of
E.coli cytoplasmic membranes was identified with this approach.
Other groups have aimed at optimizing the composition of the sample buffer for a
more efficient recovery of membrane proteins in the first dimension. In a recent study,
Luche and colleagues compared the efficiency in membrane protein solubilization of
several non-ionic, commercially available detergents (Luche, Santoni et al. 2003). The
non-ionic detergents dodecyl maltoside and decaethylene glycol mono hexadecyl
ether resulted to be the most efficient membrane protein solubilizers. Other groups
have also developed new detergents to achieve a better representation of membrane
35
proteins on 2D-gels (Gianazza, Rabilloud et al. 1987; Rabilloud, Gianazza et al.
1990).
However, membrane protein solubility problems are still encountered with 2D-PAGE.
Consequently, alternative approaches rely on one-dimensional SDS gel
electrophoresis (1D-SDS PAGE) for the separation of membrane proteins, replacing
the IEF by another separation technique in the first dimension.
2.6.1.2. Combination of chromatography and 1D SDS-PAGE
The solubility problem of membrane proteins during the IEF step in 2D-PAGE
stimulates the search for alternative separation methods, orthogonal to SDS-PAGE.
Complex protein mixtures can be separated according to several parameters, including
the retention on a chromatographic column and/or the molecular weight. The
separation of individual fractions after chromatography by means of 1D-SDS-PAGE
generates two- or multi-dimensional patterns and facilitates the resolution of different
proteins.
A wide variety of high-performance liquid chromatography (HPLC) systems has been
employed originally for the purification of membrane proteins (Welling, van der Zee
et al. 1987; Thomas and McNamee 1990). These include size-exclusion-HPLC, ion-
exchange-HPLC, bioaffinity chromatography, reverse-phase-HPLC (RP-HPLC) and
hydroxyapatite-HPLC (HAP-HPLC) with SDS. Since those chromatographic methods
allow the separation of contaminants from membrane proteins, the question arises,
whether one can also part different membrane proteins contained in a membrane
extract applying chromatography in the first dimension.
Horigome and colleagues combined ceramic HAP-HPLC with 1D-SDS-PAGE for the
investigation of membrane proteins isolated from rat erythrocyte membranes and rat
36
liver microsomes (Horigome, Hiranuma et al. 1989). The HAP-HPLC was performed
with a buffer system, which contained 1% of SDS and sodium phosphate up to a
concentration of 0.5 M. In another study, membrane proteins from rat liver rough
microsomes were efficiently resolved with a protein recovery of more than 90% by
HAP-HPLC, using 1% sodium cholate as detergent (Ichimura, Ikuta et al. 1995).
Hydroxyapatite chromatography was introduced in 1956 by Tiselius (Hjerten, Levin
et al. 1956). In HAP-HPLC, biomolecules are separated according to their different
interactions with hydroxyapatite, whose molecular formula is Ca10(PO4)6(OH)2.
Positively charged ammonium groups (e.g., side chains of lysine residues) are
attracted by the phosphate groups on the column and repelled by the calcium ions; the
situation is the opposite for carboxylic acid groups (Gorbunoff 1984; Gorbunoff 1984;
Gorbunoff and Timasheff 1984).
HAP-HPLC allows the use of strong detergents, which is very advantageous when
working with membrane proteins. However, the same proteins are often found in
more than one fraction, thus hampering the overall resolution of the two-dimensional
separation process. Excellent resolution is imperative, if gel-based methods are to be
used for the comparison of protein abundance in different samples.
In 1988, a group reported about the development of the chromatophoresis process®
which couples RP-HPLC to SDS-PAGE in a real-time automated system (Burton,
Nugent et al. 1988; Nugent, Burton et al. 1988). The real potential of this method
remains to be demonstrated.
Using SDS-PAGE for the one-dimensional separation of membrane proteins and
micro capillary liquid chromatography-electrospray ionization tandem mass-
spectrometry (µLC-ESI-MS/MS) for the analysis of peptides generated by digesting
the protein migrating to a particular zone of the gel, Simpson and co-workers
37
identified 284 proteins, including 92 membrane proteins (Simpson, Connolly et al.
2000). Although this method is suitable for cataloguing proteins contained in
membrane fractions, it is inherently not quantitative and therefore not suitable for the
detection of differences in the membrane-protein profile of cells representing different
states.
Sharov and colleagues developed a two-dimensional method for the characterization
of posttranslational modifications of rabbit sarco/endoplasmic reticulum Ca-ATPases
(SERCA) using a combination of RP-HPLC and 1D-SDS-PAGE followed by liquid
chromatography tandem mass spectrometry (LC-MS/MS) analysis of in-gel tryptic
digests (Sharov, Galeva et al. 2002). The SERCA, which belong to a group of large
hydrophobic proteins, are likely to be underrepresented on 2D-PAGE because of
reasons outlined in the preceding section. Applying this method, SERCA proteins
were successfully separated from other proteins present in native sarcoplasmic
reticulum vesicles. The degree of artificially induced posttranslational modifications
of SERCA was investigated by tandem mass spectrometric analysis. The authors
claim that their method might be applicable to the proteomic analysis of membrane
proteins of whole tissue or selected organelles, especially when posttranslational
modifications need to be monitored.
More in general, the combination of (i) fractionation of sub-cellular organelles (ii)
chromatography in the presence of SDS and (iii) 1D-SDS-PAGE appears to be a
robust avenue for the comparison of relative protein abundance in different
cells/tissues. Automation of proteome analysis will be important in order to increase
interlaboratory reproducibility and to make the process less labor-intensive.
Furthermore, automation would increase the overall throughput of sample analysis.
38
2.6.2. Evolution of proteomics: gel-free mass spectrometry-based methods
Even when protein bands (or spots) are well resolved in polyacrylamide gel
electrophoresis, the routine use of this technique for the comparison of the relative
abundance of proteins in different samples is limited to the detection of big changes
(e.g., > 3-fold change in relative abundance). Special techniques [such as biosynthetic
or post-separation isotope labeling, 2D-difference fluorescence gel electrophoresis
(2D-DIGE)] are not always compatible with the requirements for the discovery of
novel markers of pathology, but have been used with success for the studies of cells
cultured in different conditions (Aebersold and Mann 2003). Furthermore,
methodologies such as 2D-DIGE are labor intensive and do not easily lend themselves
to the analysis and comparison of several dozens of samples.
2.6.2.1. Multidimensional protein identification Technique (MudPIT)
The continuous advances in the field of biological mass spectrometry has now made it
possible to routinely identify proteins from the corresponding endoproteolytic
peptides, at total protein amounts in the femtomole range. The combined use of
multidimensional liquid chromatography (LC) and tandem mass spectrometry
(MS/MS) has made it possible to identify hundreds of proteins in the same sample
containing tryptic peptides (Link, Eng et al. 1999). In a large-scale analysis of the
yeast proteome, about 1500 proteins could be identified, more than 100 of these being
membrane proteins (Washburn, Wolters et al. 2001). More recently, a modified
methodology has been developed for the application of mass spectrometry to the
study of membrane proteins (Wu, MacCoss et al. 2003). A combination of membrane
sheets enrichment at high pH, followed by Proteinase K treatment and LC/MS
39
analysis, has allowed the identification of > 400 membrane proteins in a rat brain
homogenate. At present, however, these methodologies do not yield information
about relative protein quantity and cannot be used for the comparison of membrane
protein abundance in different complex specimens.
2.6.2.2. Combined fractional diagonal chromatography (COFRADIC™)
The profiling of complex proteomes by LC-MS/MS is complicated by the very large
number of redundant peptides. Theoretically, one unique peptide would be sufficient
to identify unambiguously each parent protein. If such unique peptides could be
isolated, the complexity of the samples could be reduced by one or two orders of
magnitude considered that tryptic digestion generates several dozen peptides per
protein (Zhang, Yan et al. 2004). In 2002, the group of Joël Vandekerckhove
introduced a peptide-based protein identification technique termed “combined
fractional diagonal chromatography” (COFRADIC™) which allows the selective
enrichment of peptides containing unique (N-terminal) or rare (Cys, Met) amino acids
(Gevaert, Van Damme et al. 2002). COFRADIC™ separates analytes in two
sequential RP-HPLC runs. Between the two runs, fractions collected from the first
dimension are chemically modified in a way that alters the retention time of the
peptides containing the target amino acid. Each fraction is then analyzed in the second
dimension using the same chromatographic conditions as in the first. Consequently,
all modified peptides exhibit altered retention times and are thus known to contain the
targeted amino acid whereas the remaining peptides elute from the column with the
same retention time as in the first dimension. The modified peptides can then be
specifically collected for further LC-MS/MS analysis. Gevaert and colleagues
successfully applied COFRADIC™ to the analysis of methionine-containing peptides
40
in a total unfractionated cell lysate obtained from E.coli K12 cells (Gevaert, Van
Damme et al. 2002). More than 800 proteins were identified including abundant, rare,
large, small, acidic, basic and hydrophobic proteins. In another experiment, the same
group analyzed N-terminal peptides isolated from both a cytosolic and membrane-
cytoskeleton fraction of human thrombocytes (Gevaert, Goethals et al. 2003). Free
amino groups were first blocked by acetylation and then digested with trypsin. After
RP-HPLC of the generated peptides, internal peptides were blocked using 2,4,6-
trinitrobenzenesulfonic acid; the blocked internal peptides displayed a strong
hydrophobic shift and therefore segregated from the unaltered N-terminal peptides
during a second identical separation step. More recently, Gevaert et al. specifically
isolated cysteine-containing peptides in samples obtained from human platelets and
enriched human plasma (Gevaert, Ghesquiere et al. 2004).
The COFRADIC™ technique features a high dynamic range (the simultaneous
detection of proteins present in ratios of 1:10,000 is possible) coupled with the ability
of isolating large numbers of membrane proteins. However, it remains to be seen
whether COFRADIC™ can be applied to perform a relative quantification in two
closely related protein samples. This goal may be facilitated by the stable
incorporation of 18O at newly formed carboxy termini that are generated by
proteolytic cleavage between the two COFRADIC™ runs (Gevaert, Ghesquiere et al.
2004).
2.6.2.3. Isotope-coded affinity tags (ICAT)
In 1999, Aebersold and coworkers introduced the concept of isotope-coded affinity
tags (“ICAT”) for the stable non-radioactive isotopic labeling of proteins, compatible
with protein identification and relative quantification in different biological specimens
41
(Gygi, Rist et al. 1999). In its original implementation, the ICAT technology consists
in the biotinylation of cysteine residues in proteins with reactive derivatives of biotin,
carrying a linker arm with hydrogen or deuterium atoms. These “light” and “heavy”
biotin derivatives serve a dual purpose. First, they allow reducing the complexity of
tryptic peptides to be analyzed in a gel-free mass-spectrometry experiments (they can
be purified on affinity resins; only few tryptic peptides in a protein contain a cysteine
residue). Second, the labeling of peptides from two different samples with a light or
heavy tag allows the use of LC-MS/MS methodologies for the relative comparison of
protein abundance in the two samples. The relative protein abundance is in fact
reflected in the relative intensity of the mass spectrometry signals of the
corresponding biotinylated peptides, which are separated in the m/z axis by the
number of Daltons corresponding to the number of atoms that are either hydrogen or
deuterium in the biotin derivative tag. A number of modified ICAT implementations
have been developed in the last few years. They include the use of ICAT for the
relative quantization of spots in 2D gels (Smolka, Zhou et al. 2002), the use of solid-
phase isotope tagging (Zhou, Ranish et al. 2002) and the use of special chemical
procedures for the analysis of post-translational modifications, such as glycosylation
and phosphorylation (Zhou, Watts et al. 2001).
The ICAT technology has recently been used for the quantitative profiling of
differentiation-induced microsomal proteins. The method was used to identify and
determine the ratios of abundance of each of 491 proteins contained in the
microsomal fractions of naïve and in vitro-differentiated human myeloid leukemia
cells (Han, Eng et al. 2001). In this study, the authors recognize that a subset of
proteins that lack cysteine residues, very low abundant proteins and very hydrophobic
proteins would not be analyzed with this technique.
42
The substitution of a thiol-reactive ICAT reagent with a similar ICAT reagent,
capable of reaction with primary amino groups is not straightforward. The higher
number of lysine residues in proteins (compared to cysteines) may lead to incomplete
chemical coupling, and to a distribution of patterns of (incomplete) lysine labeling,
thus complicating the down-stream LC-MS/MS analysis.
2.6.2.4. iTRAQ™ Reagents
At the 6th Siena Proteomic Meeting “From genome to proteome”, in September 2004,
Applied Biosystems reported about the development of a multiple set of four isobaric
iTRAQ™ Reagents 114, 115, 116 and 117 which are amine specific and yield labeled
peptides which are identical in mass and hence also identical in single MS mode, but
which produce strong, diagnostic, low-mass MS/MS signature ions allowing for
quantization of up to four different samples simultaneously. iTRAQ™ Reagents
consist of a reporter group, a balance group, and a peptide reactive group. The peptide
reactive group covalently links an iTRAQ™ Reagent isobaric tag with each lysine
side chain and N-terminus group of a peptide, labeling all peptides in a given sample
digest. The balance group ensures that an iTRAQ™ Reagent-labeled peptide displays
the same mass, whether bound to iTRAQ™ reagent 114, 115, 116, or 117. During
MS/MS, the isobaric tag cleaves and because of fragmentation, there is neutral loss of
the balance group. The iTRAQ™ reporter groups are generated, displaying diagnostic
ions in the low-mass region between m/z of 114 – 117.
With reagents that label amines instead of thiols, the overall protein and proteome
coverage is improved, while posttranslational modification (PTM) information is
retained. Specific proteins of interest can be quantified in absolute terms by including
labeled internal standard peptides representative for the protein of interest. In contrast
43
to ICAT reagents, which label cysteines prior to protein digestion, iTRAQ™
Reagents react with peptides derived from proteolytically digested protein samples.
Labeling peptides instead of proteins features several advantages: first, peptides are
more soluble than proteins. It is therefore likely, that peptides representative for large
hydrophobic proteins (e.g. membrane proteins), which would escape analysis in a
protein labeling experiment are detected in a peptide labeling experiment. Second,
quantitative labeling is more feasible with peptides than with proteins, since the target
amino groups are easier accessible in peptides compared to proteins.
iTRAQ™ appears to be an interesting alternative to ICAT, however, its potential in
terms of dynamic range and detection of “difficult” proteins (e.g. membrane proteins)
remains to be evaluated.
A conceptually similar approach for the accurate quantification of peptides and
proteins has been published by Thompson and colleagues in 2003 (Thompson,
Schafer et al. 2003).
2.6.2.5. Two-dimensional peptide mapping
Stimulated by the work of Schrader, Schulz-Knappe and colleagues (Schulz-Knappe,
Zucht et al. 2001; Tammen, Hess et al. 2002) which have routinely used the
orthogonal combination of chromatography and matrix assisted laser desorption
ionization-time of flight mass spectrometry (MALDI-TOF MS) for the relative
quantization of peptides in biological fluids (e.g., sera and cerebrospinal fluids), we
have set up a method for the simultaneous recovery, separation, identification and
relative quantization of membrane proteins isolated from cultured mammalian cells
termed two-dimensional peptide mapping (2D-PM).
44
In the course of 2D-PM, cells are biotinylated with a biotin reagent reactive for
primary amino groups followed by the isolation of biotinylated proteins on
streptavidin. The purified biotinylated proteins are either eluted from streptavidin and
digested with trypsin or directly digested on the streptavidin-coated resin. Resulting
peptides are separated by microcapillary HPLC on a C18 column, followed by the
analysis of the eluted fractions with matrix assisted laser desorption ionization mass
spectrometry (MALDI-TOF MS). The resulting data is used to establish a two-
dimensional peptide map (2D peptide map) reporting the HPLC fractions on the y-
axis and the m/z ratios of the measured peptides on the x-axis. The mass peaks signal
intensities are displayed by means of a grayscale. The grayscale is standardized to an
internal standard peptide, which is added to each chromatographic fraction in a known
amount prior to the MALDI-TOF MS experiment. The 2D peptide map allows the
immediate appreciation of the entire mass spectrometric profile of the
chromatographic fractions, but allows also a comparison analysis of the same HPLC
fraction derived from two different samples (e.g., control and treatment). Interesting
fractions are used for subsequent tandem mass spectrometry measurements in order to
identify differentially expressed proteins.
It is almost certain that modern mass spectrometric procedures and instrumentation
[(such as the use of Fourier transform ion cyclotron (FT-ICR) and MALDI-TOF time
of flight (MALDI-TOF-TOF) spectrometers (Aebersold and Mann 2003)], which
offer unprecedented resolution and sensitivity, will contribute to the increased use of
mass spectrometry-based methods for gel-free proteomic analysis. However, the study
of membrane proteins will also require improved methodologies for the chemical
modification and recovery of peptides from these low abundant, hydrophobic
proteins.
45
2.6.3. Ligand based methods
Traditionally, the first markers of angiogenesis have been discovered either by limited
proteolysis of purified protein preparations (Zardi, Carnemolla et al. 1987), or by
animal immunization with biological samples derived from tumors, followed by an
extensive immunohistochemical analysis of the resulting hybridomas (Liu, Moy et al.
1997; Chang, O'Keefe et al. 1999). The introduction of recombinant antibody
technologies, and in particular of antibody phage technology (Winter, Griffiths et al.
1994), has greatly facilitated the production of good-quality monoclonal antibodies
without immunization. These technologies are particularly efficient when pure antigen
preparations are available (Viti, Nilsson et al. 2000), but antibodies have also been
generated from phage libraries against “difficult” antigens (Hoogenboom, Lutgerink
et al. 1999).
It is difficult to imagine that antibody-based chips may facilitate the study of the
relative abundance of membrane proteins in different biological specimens (Elia,
Silacci et al. 2002). Nonetheless, if larger public-domain collections of monoclonal
antibodies become available in the future, it should be possible to use fluorescence-
activated cell sorters (FACS) and/or immunohistochemistry with tissue arrays
(Schraml, Kononen et al. 1999) for the relative quantization of membrane proteins in
different cells/tissues.
Ruoslahti, Pasqualini and co-workers have pioneered the in vivo biopanning of
peptide phage libraries, in an attempt to identify binding specificities against different
vascular addresses in different tissues and/or tumors (Pasqualini and Ruoslahti 1996;
Rajotte, Arap et al. 1998). Among others, peptides specific to integrins and to CD13
were identified with this procedure. However, the real potential of this technology
46
remains to be demonstrated, considering that the use of peptides on tissue sections is
often less efficient than the use of antibodies (which normally display a higher affinity
for the antigen), and in the absence of quantitative biodistribution studies and clinical
studies with purified preparations of the vascular-targeting peptides.
2.6.4. Application of proteomic methods to the identification of new targets for tumor
therapy
As already mentioned above, a number of studies have been carried out by proteomic
profiling in order to discover novel targets for therapy of cancer. While a
comprehensive review of the studies carried out would be beyond the scope of this
section, a couple of examples of proteomic investigations comparing malignant and
nonmalignant cell lines are worth mentioning.
Enhanced levels of urokinase plasminogen activator (uPA) and urokinase
plasminogen activator receptor (uPAR) are possibly the strongest indicator of poor
prognosis in mammary carcinoma (Ganesh, Sier et al. 1994; Duffy 2002). By
transfecting a highly metastatic colon carcinoma cell line with an antisense 5’uPAR
cDNA fragment, Ahmed and colleagues (Ahmed, Oliva et al. 2003) silenced the cell
surface expression of uPAR, obtaining a regression of the metastatic phenotype. A
proteomic comparison of the uPAR-silenced cell line and their wild type counterpart
allowed the identification of more that 300 proteins modulated by uPAR silencing,
some of which could represent valuable targets for metastasis inhibition.
Cravatt and colleagues (Jessani, Humphrey et al. 2004) carried out a functional
proteomic analysis, using a suite of activity-based chemical probes, on the human
breast cancer line MDA-MB-231 grown either in culture or as orthotopic xenografts
47
in the mammary fat pad of immunodeficient mice. Cells isolated from tumors
exhibited profound differences in their enzyme activity profile compared with the
parental cell line, including the dramatic posttranscriptional up-regulation of uPA and
of tissue plasminogen activator and down-regulation of the glycolytic enzyme
phosphofructokinase.
uPA and uPAR appear then to be interesting targets for molecular intervention, but
the pharmaceutical development of inhibitory molecules (either low-molecular weight
compounds or therapeutic antibodies) is still in its infancy. Strategies based on
antisense vectors, siRNA (Arens, Gandhari et al. 2005), as well as linear and cyclic
peptides (Magdolen, Burgle et al. 2001; Ploug, Ostergaard et al. 2001) have been
proposed. Furthermore, efforts have been made to raise antibodies against different
components of the urokinase complex to limit tumor progression. However, the
potential of these antibodies may not have been fully exploited because of insufficient
knowledge about the localization of the epitopes. Recently, experiments aimed at
mapping the epitopes for a series of monoclonal antibodies to uPA, directed against
either the kringle or the serine protease domain have shown that different
functionalities of the enzyme may be modulated by interfering with the appropriate
epitope (Petersen, Hansen et al. 2001).
2.7. From in vitro to in vivo model systems for the identification of vascular
targets
48
In principle, the most direct way to assess differences in protein abundance between
the tumor endothelium and the normal endothelium would consist in the in vivo
labeling of vascular structures, followed by rapid recovery and comparative proteomic
analysis of the proteins in the two samples.
The group of Jan Schnitzer has pioneered the use of colloidal silica for the in vivo
coating of vascular structures in tumors and in normal organs (Jacobson, Schnitzer et
al. 1992; Czarny, Liu et al. 2003). This physical modification allows the recovery (by
centrifugation and fractionation) of silica-coated structures (luminal cell plasma
membranes and caveolae of the endothelium), providing ideal material for proteomic
investigations, for example by immunization (McIntosh, Tan et al. 2002) or by 2D-
PAGE. Combining the colloidal silica based subcellular fractionation procedure with
subsequent differential proteomic analysis of luminal endothelial cell plasma
membranes and caveolae isolated from normal rat organs or tumors, Oh and
colleagues discovered aminopeptidase-P and annexin A1 as selective in vivo targets
for antibodies in lungs and solid tumors, respectively (Oh, Li et al. 2004).
De la Fuente et al. have described the artificial perfusion of lungs isolated from
normal and hyperoxic rats with Sulfo-NHS-LC-biotin (De La Fuente, Dawson et al.
1997). After SDS-PAGE, the biotinylated proteins were visualized using a
chemiluminescence substrate for the streptavidin-horseradish peroxidase conjugate,
outlining differences in rats exposed to hyperoxia for 48-60 hours.
Our group is using the terminal perfusion of tumor-bearing mice with sulfo-NHS-LC-
biotin solution as an avenue for the in vivo covalent modification of amine-containing
phospholipids and proteins, which are accessible to the reagent during the perfusion
(Rybak, Scheurer et al. 2004; Rybak, Ettorre et al. 2005). After anesthesia, mice are
first perfused with saline solution, to remove circulating cells, proteins and other
49
primary-amine containing compounds. Few minutes later, perfusion is continued with
an aqueous solution of sulfo-NHS-LC-biotin, followed by a primary amine (e.g., Tris
buffer) to quench unreacted ester derivatives of biotin. This methodology leads to
reliable and efficient labeling of accessible structures (mainly vascular structures) in
vivo, and is ideally suited for proteomic investigations. The resulting biotinylated
proteins (or the corresponding peptides generated by endoproteolytic cleavage) can be
purified on streptavidin-coated resins in the presence of SDS. However, the choice of
detergent and of purification protocol depends on the experimental strategy chosen for
proteomic investigations. In principle, the in vivo biotinylation method presents
several attractive features, as it allows a direct investigation of those accessible
targets, which are likely to be amenable to targeted anticancer imaging and
therapeutic strategies. As protein biotinylation lends itself not only to purification
strategies (special precautions for elution must be chosen, considering the high-
affinity interaction with streptavidin) (Rybak, Scheurer et al. 2004), but also to
biochemical analysis (e.g., by blotting or microscopic analysis with streptavidin-based
detection reagents), it is possible to monitor the efficiency of the biotinylation
reaction in various organs, prior to proteomic analysis.
50
3. Results & Discussion
3.1. Modulation of gene expression by extracellular pH variations in human
fibroblasts. A transcriptomic and proteomic study.
Physiological variations of pH values are observed during development (Baltz 1993;
Gilbert 2000), in physiological processes and in disease (e.g., tumor cells often create
an acidic extracellular environment, but display basic intracellular pH as a
consequence of increased metabolic activity) (Stubbs, McSheehy et al. 2000).
Intracellular and extracellular pH values control the proliferation of fibroblasts, their
response to growth factors and their processing of primary transcripts of extracellular
matrix components (Borsi, Allemanni et al. 1996). For example, the pattern of
alternative splicing of tenascin-C (TN-C) is tightly regulated by the pH at which
primary fibroblast cell cultures are grown. While in the pH range 6.7 – 6.9 the small
isoform of TN-C is preferentially expressed, at pH values above 7.2 the large TN-C
isoform (containing 8 extra fibronectin type-III homology repeats) becomes
predominant (Borsi, Balza et al. 1995). The large TN-C isoform is an oncofetal
marker, which is over-expressed in a variety of solid tumors and which is the target of
monoclonal antibodies being used in anticancer therapeutic strategies (Paganelli,
Bartolomei et al. 2001; Reardon, Akabani et al. 2002). Interestingly, malignantly
transformed fibroblasts predominantly express the large TN-C isoform independent of
the culture medium pH, since these cells are able to maintain a basic intracellular pH
under a broad range of culture conditions (Borsi, Allemanni et al. 1996).
Changes in pH are known to regulate gene expression in different cell types
(Petronini, Alfieri et al. 1995; Shi, Le et al. 2001). However, little is known about the
51
regulation of gene expression in fibroblasts as a function of pH fluctuations, such as
those occurring in physiological and pathological processes.
In this thesis, we have performed a comprehensive analysis of the patterns of gene
expression in normal human dermal fibroblasts (NHDF) at two different pH values (in
the presence and in the absence of serum), with the aim of getting a deeper insight in
the regulation of the transcriptional program of fibroblasts as a response to pH
change. Primary fibroblast cell cultures were chosen as model system, since these
cells adapt their intracellular pH on the basis of the pH of the culture medium (Austin
and Wray 1993; Mellergard, Ou-Yang et al. 1994; Borsi, Allemanni et al. 1996), and
since they are responsible for the synthesis of a large portion of ECM components in
health and disease. Furthermore, since our interest mainly lies in the identification of
components of the ECM, which are aberrantly expressed in the tumor environment,
we have also used 2D-PAGE and mass spectrometry to identify proteins, which are
differentially secreted by NHDF cultured at pH 6.7 and 7.5. In the past, our group (in
collaboration with the group of L. Zardi, Genova, Italy) has used components of the
modified ECM, whose expression is regulated by pH, as targets for antibody-based
anti-cancer strategies (Birchler, Neri et al. 1999; Carnemolla, Castellani et al. 1999;
Tarli, Balza et al. 1999; Viti, Tarli et al. 1999; Nilsson, Kosmehl et al. 2001;
Carnemolla, Borsi et al. 2002; Halin, Rondini et al. 2002), which are currently being
investigated in clinical studies.
3.1.1. Genome-wide analysis of mRNA levels in NHDF cultured at different pH
We have aimed at performing a genome-wide analysis of the modulation of gene
expression in fibroblasts, as a consequence of a change in the pH of the culture
medium, in the presence or absence of serum.
52
The decision of including serum starvation among the variables of our experimental
design was stimulated by the fact that acidosis and nutrient starvation are often
associated in physiological and pathological processes. Furthermore, when studying
the pattern of alternative splicing of the TN-C transcript in fibroblasts as a function of
culture pH, we had observed (as previously reported by Luciano Zardi) that the
Figure 1: Northern Blot analysis of the expression of TN-C and GAPDH in NHDF cultured at pH 6.7 or 7.5, in the presence or absence of serum. Zardi and coworkers had previously reported that modulations of the extracellular pH have an effect on the pattern of alternative splicing of transcripts coding for TN-C in NHDF in in vitro culture conditions (Borsi, Balza et al. 1995; Borsi, Allemanni et al. 1996). When cultured at pH 7.5, normal fibroblasts mainly express a “large” form of the transcript, of about 8 kb, coding for a protein isoform containing 14.5 EGF-like and 16 FN type III repeats. With increasing acidity of the culture medium, cells begin to express also a “small” form of the transcript, of about 6.8 kb, which becomes predominant at pH 6.7-6.6. pH appeared to control the pattern of splicing for cells grown both in the presence or in the absence of serum (Borsi, Allemanni et al. 1996). However, serum stimulation of quiescent fibroblasts had been reported to
result in a dynamic modulation of the patterns of alternative splicing of the primary TN-C transcript (Borsi, Balza et al. 1994). We have reproduced the findings described above, observing that the large isoform of TN-C is preferentially expressed in NHDF grown at basic pH. However, we found that the removal of serum reduced the expression of the large TN-C isoform, independent of culture pH. large TN-C isoform is preferentially expressed at basic pH (Figure 1). However, we
had also found that the removal of serum from the culture medium reduced the
expression of the large TN-C isoform, independent of the culture pH.
The analysis of the effect of pH on fibroblast gene expression in the presence or the
absence of serum was performed according to the experimental scheme depicted in
Figure 2. Fibroblasts were grown at pH 7.5 in DMEM medium containing fetal
bovine serum (FBS) until confluence. At that point, replicate flasks were washed and
the medium replaced with fresh DMEM, buffered at pH 6.7 or 7.5, in the presence or
absence of 10% FBS. After changing the medium to the two different pH values, the
53
Figure 2: Experimental design.
t the end of the incubation time, cells were harvested and used for total RNA extraction. Media collected after 72 ours incubation of NHDF at pH 6.7 or 7.5 in the absence of FBS were concentrated 800-fold by ultrafiltration and
used for 2D-PAGE analysis.
confluent cells did not change morphology, and for both conditions, cell mortality was
constantly below 5%. At different time points, total RNA was extracted and used for
the synthesis of biotinylated cRNA, in order to allow the quantization of gene
expression with the Affymetrix technology (Lipshutz, Fodor et al. 1999; Lipshutz
2000)
In a first experimental set up, we have compared the levels of transcripts in NHDF
cells grown in the presence of 10% FBS at different time points (24, 48 and 72 hours)
Replicate flasks were seeded with NHDF and cells were grown until confluence. Monolayers were washed (t = 0) and exposed for 24, 48 or 72 hours to DMEM buffered at pH 6.7 or 7.5, in the presence or absence of 10% FBS. Ah
54
Figure 3: Comparative analysis of NHDF gene expression modulation as a consequence of cell exposure to acidic medium. Triplicate, independent RNA preparations were obtained from NHDF grown at pH 6.7 for 24, 48 and 72 hours or at pH 7.5 for 24 hours, in the presence of serum. Using the Affymetrix oligonucleotide microarray technology, a measurement of expression levels for 12,565 genes (represented on the Affymetrix HG-U95A ver 2 gene chip) was carried out. Comparative analysis of gene expression levels in NHDF exposed to pH 6.7 vs. pH 7.5 was performed by GeneSpring software. Replicates were averaged, and a per gene normalization was carried out, meaning that the average values of gene expression in samples of fibroblasts grown at pH 6.7 were used to calculate (for each gene and experimental condition) ratios relative to the average of the expression levels of the same gene in samples of fibroblasts grown at pH 7.5 for 24 hours. 2,068 out of 12,565 genes resulted to be modulated by more than two-fold (up-regulation or down-regulation) in at least one of the three time points. Figure 3A presents a hierarchical clustering of such 2,068 genes, according to the method of Eisen (Eisen, Spellman et al. 1998). Genes are grouped based on the “similarity” of their expression pattern along the experimental time dimension and are shown as horizontal bars of different colors. The similarity tree is displayed in the left portion of the figure. The green-to-red color grading shown on the right-hand side of panel A represents the ratios of gene expression levels for the different time points of NHDF cells grown at pH 6.7, relative to the corresponding gene expression levels at pH 7.5. Figure 3B presents the K-means clustering (Dougherty, Barrera et al. 2002) of the same genes in 7 classes, according to the profile of up-regulation or down-regulation of gene expression as a function of time. A comprehensive list of the expression levels can be found in the supplementary information [Supplementary Table 1, available at http://www.pharma.ethz.ch/bmm/div/NHDF/].
55
after changing the pH medium to pH 6.7, relative to control cells maintained at pH
7.5.
At each time point, the absolute expression levels for the more than 12,000
represented genes were measured using the Affymetrix Micro Array Suite 2.0
software, starting from NHDF cell preparations performed in triplicate. The
comparative analysis of gene expression was performed using the Silicon Genetics
GeneSpring version 4.2.1 software, as described in the Materials and Methods
section.
2,068 out of 12,565 genes resulted to be modulated by more than two-fold (up-
regulation or down-regulation) in at least one of the three time points. Figure 3A
presents a hierarchical clustering of such 2,068 genes, automatically performed by the
GeneSpring software according to the method of Eisen (Eisen, Spellman et al. 1998).
Genes are grouped on the basis of the “similarity” of their expression pattern along
the experimental time dimension. The similarity tree is displayed in the left portion of
the figure. A green-to-red color grading represents the ratios of gene expression levels
for the different time points of NHDF cells grown at pH 6.7, relative to the
corresponding gene expression levels at pH 7.5 (i.e., ratio > 1 for enhanced gene
expression at acidic pH). Figure 3B presents the K-means clustering (Dougherty,
Barrera et al. 2002) of the same genes in 7 classes, according to the profile of up-
regulation or down-regulation of gene expression as a function of time. A
comprehensive list of the expression levels can be found in Supplementary Table 1,
[available at the website http://www.pharma.ethz.ch/bmm/div/NHDF/]. Sixty-seven
out of 2,068 genes showed a modulation of expression > 5-fold at 72 hours (the ratio
being < 0.2 or > 5).
56
In Figure 4, genes showing a modulated expression pattern were grouped into eight
different ontological groups: ECM proteins (Fig. 4A), cell cycle regulators (Fig. 4B),
apoptosis regulators (Fig. 4C), growth factor receptors (Fig. 4D), growth factors (Fig.
4E), DNA-binding proteins/transcription factors (Fig. 4F), receptors (Fig. 4G) and
ligands (Fig. 4H).
At pH 6.7, strongly over-expressed genes of ECM components include microfibril-
associated glycoprotein 4 (Zhao, Lee et al. 1995), EFEMP-1 [a fibulin-like protein
containing five EGF-like domains (Ikegawa, Toda et al. 1996)] and UDP-GAL:β-
GLCNAC: β-1,3-galactosyl transferase polypeptide 4 (Amado, Almeida et al. 1998).
Down-regulated genes include the matrix metalloproteases 12 [macrophage elastase,
(Belaaouaj, Shipley et al. 1995)] and 3 [stromelysin 1 (Wilhelm, Collier et al. 1987)],
the elastin precursor (Rosenbloom, Bashir et al. 1991) and the prolyl 4-hydroxylase
alpha (II) subunit (Annunen, Helaakoski et al. 1997) (Figure 4A).
An overall reduction of expression of cell cycle regulator genes, like cyclins A2, B1,
B2, E2, and CDC2 (Furukawa 2002) as well as an increase in the levels of cyclin-
dependent kinase inhibitor p57KIP2 (Lee, Reynisdottir et al. 1995), correlates with
the observation that the switch to a more acidic environment promotes an arrest in
NHDF cell cycle progression (Borsi, Allemanni et al. 1996) (Figure 4B). A decrease
in expression of survival factors, such as survivin (Ambrosini, Adida et al. 1997) and
increased levels of the apoptosis-specific protein ASP (Grand, Milner et al. 1995) are
counterbalanced by the concomitant increase in the expression of HIAP-1 (Liston,
Roy et al. 1996) and of an isoform of caspase-like apoptosis regulator protein 2
(Inohara, Koseki et al. 1997) (Figure 4C). Some genes encoding for growth factor
57
Figure 4: Clustering of NHDF genes modulated at pH 6.7. A simplified gene ontology was automatically built up by the GeneSpring software and the 2,068 genes whose expression is modulated by exposure to acidic medium were clustered accordingly. In the figure, the time course (24, 48 and 72 hours) of expression of genes coding for proteins of the extracellular matrix (A), for cell-cycle (B) and apoptosis (C) regulators, for growth factor receptors (D) and growth factor receptor ligands (E), for DNA-binding proteins and transcription factors (F) or for receptors (G) and ligands (H) are shown, together with the relative Affymetrix gene identifier and the gene systematic name (GeneSpring, Silicon Genetics, Redwood City, CA, USA; http://www.sigenetics.com). Genes that are mentioned in the text are outlined in boldface. In the lower part of the figure, a green-to-red color grading represents the ratios of gene expression levels for the different time points of NHDF cells grown at pH 6.7, relative to the corresponding gene expression levels at pH 7.5. Further information about the indicated genes, as well as the sequences of oligonucleotides employed by Affymetrix for recognition of the different genes is available at the Affymetrix website (http://www.affymetrix.com/analysis/index.affx)
58
Figure 4: Clustering of NHDF genes modulated at pH 6.7 (Continued).
receptors show an increase in their expression level at acidic pH, namely a platelet-
derived growth factor receptor-like coding gene (Fujiwara, Ohata et al. 1995) and a
growth factor inducible nuclear protein N10 (Chang, Kokontis et al. 1989) (Figure
59
4D). Among the growth factors and growth factor-related genes (Figure 4E), a
markedly increased expression was observed for the insulin-like growth factor
binding proteins (IGFBP) 2 (Agarwal, Hsieh et al. 1991) and, to a lower extent, for
IGFBP-5 (Allander, Larsson et al. 1994) and the keratinocyte growth factor (Rubin,
Osada et al. 1989). A reduction in expression was observed, in contrast, for heregulin
[glial growth factor 2 (Holmes, Sliwkowski et al. 1992)], for basic fibroblast growth
factor (Abraham, Whang et al. 1986) and for the insulin-like growth factor binding
protein 3 (Ferry, Cerri et al. 1999), this last, however, being expressed at very high
absolute levels in both NHDF cultured at pH 6.7 and pH 7.5. A variety of modulated
genes can be observed among DNA-binding proteins/transcription factors (Fig. 4F),
receptors (Fig. 4G) and ligands (Fig. 4H). One of the strikingly over-expressed genes
at pH 6.7 is stanniocalcin 1 (Olsen, Cepeda et al. 1996), a calcium and phosphate
homeostasis regulating hormone (Figure 4H).
3.1.2. Modulation in the expression of ECM components in NHDF after serum
starvation and/or pH change
The Affymetrix gene chip system was also used to investigate whether the modulation
of gene expression, following a change of the culture medium pH, was influenced by
the concomitant removal of serum. Our analysis was mainly focused on ECM
components, since those which display a restricted pattern of expression in healthy
tissues but not in disease may provide excellent targets for biomolecular intervention
(Birchler, Neri et al. 1999; Carnemolla, Castellani et al. 1999; Tarli, Balza et al. 1999;
Viti, Tarli et al. 1999; Nilsson, Kosmehl et al. 2001; Carnemolla, Borsi et al. 2002;
Halin, Rondini et al. 2002). However, a complete list of modulated genes is provided
in Supplementary Table 2 [http://www.pharma.ethz.ch/bmm/div/NHDF/]. The most
60
Figure 5: Effect of change of culture medium pH, of serum starvation or of their combination on ECM components gene expression levels. Levels of expression of genes coding for components of the ECM in NHDF cultured at pH 6.7 for 24, 48 or 72 hours in the absence of serum were compared to NHDF cultured at pH 7.5 for 24 hours in the presence (A) or absence (B) of serum. Panel C shows the effect of serum starvation on NHDF grown for 24, 48 or 72 hours at pH 7.5 in absence of serum, relative to control NHDF cultures, grown at pH 7.5 for 24 hours in the presence of serum. On the right part of each panel, the Affymetrix gene identifiers and gene systematic names are shown. Genes that are mentioned in the text are outlined in boldface. In the lower part of the figure, a green-to-red color grading of gene expression ratios is included.
61
striking modulations of gene expression observed for the simultaneous acidification of
the culture pH and serum withdrawal (Figure 5A), were also observed in fibroblasts
kept at pH 7.5 but deprived of serum (Figure 5C), indicating that serum starvation
dominates these transcriptional programs. The most notable up-regulations of gene
expression in both Figure 5A and 5C were observed for microfibril-associated
glycoprotein 4 (Zhao, Lee et al. 1995), collagen type XIV α1 [undulin, (Bauer,
Dieterich et al. 1997)], matrix metalloproteinase 11 [stromelysin 3, (McDonnell and
Matrisian 1990)] and extracellular matrix protein 2 (Nishiu, Tanaka et al. 1998), while
elastin precursor (Rosenbloom, Bashir et al. 1991), matrix metalloproteases 12
[macrophage elastase, (Belaaouaj, Shipley et al. 1995)] and cartilage oligomeric
matrix protein precursor (Newton, Weremowicz et al. 1994) were consistently down-
regulated. Interestingly, collagen type XIV α1 expression was strongly down
regulated upon acidification of a serum-free medium (Figure 5B), indicating that
serum starvation and pH change had opposite effects on the control of transcriptional
activity for this gene. By contrast, expression of dermal fibroblast elastin precursor
(Uitto, Christiano et al. 1991) was up-regulated upon acidification of a serum-free
medium (Figure 5B), but was strongly down-regulated by FBS removal of a serum-
rich medium, independent of culture pH (Figure 5A and 5C).
Some genes displayed a strongly modulated gene expression upon serum starvation at
pH 7.5 (Figure 5C), which was abolished by pH change (Figure 5A), collagen XV α1
(Muragaki, Abe et al. 1994) being a notable example.
3.1.3. 2D-PAGE analysis of secreted proteins expressed by NHDF cultured at
different pH
62
In order to study the modulation in the expression of proteins secreted by fibroblasts,
at the protein level, as a consequence of the acidification of the culture medium and
serum starvation, we adopted the following experimental approach. NHDF were
grown until confluence in complete DMEM medium. Cells were then incubated for
72 hours in serum-free DMEM at two different pH values (6.7 or 7.5). The resulting
supernatant, containing the proteins secreted by the fibroblasts, was collected at the
end of incubation time and concentrated 800-fold by ultrafiltration. The proteins were
Figure 6: 2D-PAGE of proteins secreted by NHDF at pH 6.7 and at pH 7.5. Concentrated supernatants from NHDF grown for 72 hours in DMEM FBS-free at pH 6.7 (A) or at pH 7.5 (B), were subjected to 2D gel electrophoresis as described in Experimental Procedures. Two representative gels are shown, in which the IPG strips pH range was 4-7. Molecular weight markers were included and their values are indicated on the left. Red circles/ellipses on both gels indicate those proteins whose expression is apparently increased at the corresponding pH value. Blue squares/rectangles indicate those proteins that were poorly modulated by a pH change. All the spots indicated by a circle/ellipse or by a square/rectangle were excised from the gel, trypsin-digested and subjected to mass spectrometric analysis. Numbering of the spots indicates the proteins that could be identified and refers to Table 1 (see text). Asterisks mark the proteins for which an unambiguous identification could not be obtained. Spots indicated with roman numbers correspond to serum contaminants. A selected, magnified area of gels from two replicate, independent samples at pH 6.7 (C, D) or at pH 7.5 (E, F) shows the reproducibility of the experimental setup. The position of the selected area is indicated in A and B by a dotted black square. Arrows in E and F point to two relevant protein spots (lamin A and tetranectin), over expressed in NHDF grown at pH 7.5. While most spots on the gels correspond to ECM components, some spots correspond to intracellular proteins, originating from cell lysis.
63
then separated by 2D-PAGE, using both wide range (3-10) and narrow range (4-7)
IPG strips. The resulting gels were then stained, scanned and compared. Relevant
protein spots (showing either constant or modulated expression) were cut and
identified using mass spectrometry.
The patterns of proteins secreted by NHDF at pH 6.7 and at pH 7.5 are shown in
Figure. 6 A and B, respectively. Spots showing a differential pattern of expression at
the two pH values are indicated with a red circle. All the experiments were repeated at
least twice and the overall 2D-PAGE spot pattern was found to be reproducible in the
different conditions, with only minor gel-to-gel variations (Figure 6 C-F).
In total, more than 650 spots were cut out of 2D gels, trypsin-digested and measured
using an LCQDeca ion trap µLC-MS/MS mass spectrometer. More than 55% (365)
could be identified. Some of them were identified only with a single peptide, but with
a good correlation coefficient by the SEQUEST algorithm. Most of these
identifications were later confirmed by measurements of the same spot in a duplicate
gel. In general, we found rarely more than one protein per spot, with the exception of
some contamination by trypsin and keratin peptides.
Table 1 shows a list of the identified proteins, with the SwissProt accession numbers,
indicating whether their expression at the two different pH values is modulated.
For many proteins, several isoforms were identified, with small differences in
molecular weight and broad pI variations (e.g. collagen VI α1, PEDF, cathepsin B).
In spite of the fact that the 2D-PAGE analysis reflected an accumulation of protein
from transcripts produced at different time points, a number of clear differences
64
# on Fig. 6 Common name SwissProt Accession
number pH 6.7
pH 7.5
1 Actin P03996/P02570 + + 2 α-Actinin 1 – F-actin cross-linking P12814 - + 3 Agrin (fragment) O00468 + - 4 Cathepsin B1 P07858 + +/- 5 Cathepsin L P07711 + +/- 6 Clusterin – complement associated protein
SP40 P10909 + + 7 Collagen I α1 (whole protein and fragment) P02452 + + 8 Collagen I α2 (fragment) P08123 + + 9 Collagen III α1 (fragment) P02461 + +
10 Collagen VI α1(whole protein and fragment) P12109 + +
11 Collagen VI α2 P12110 + + 12 Disulfide isomerase ER60 P30101 - + 13 Galectin-7 – β-galactoside binding lectin P47929 + + 14 Gelsolin – Actin depolymerizing factor P06396 +/- + 15 Glucose regulated protein 78kD – GRP78 P11021 + + 16 Glutathione-S-transferase P P09211 + + 17 L-Lactate dehydrogenase H chain P07195 + +/- 18 Lamin A (fragment) P02545 +/- + 19 Laminin γ-1 P11047 + + 20 Laminin Receptor P08865 + + 21 Lumican – Keratan sulfate proteoglycan P51884 - + 22 Matrix metalloproteinase MMP-1 § P03956 + + 23 Matrix metalloproteinase MMP-2 P08253 + + 24 Matrix metalloproteinase MMP-3 P08254 + + 25 Perlecan (fragment) P98160 + + 26 Pigment epithelium derived factor – PEDF P36955 + + 27 Procollagen I Q15113 + +/- 28 Rho-GDP dissociation inhibitor P52565 + + 29 Secreted protein acidic rich in cysteine
SPRC P09486/P07214 + + 30 Superoxide dismutase P00441 - + 31 Tetranectin – plasminogen-kringle 4
binding protein P05452 - + 32 Thioredoxin P10599 + + 33 Thioredoxin peroxidase 1 P32119 +/- +/- 34 Tropomyosin P12324 + + 35 Vimentin (fragment) P08670 + + 36 Vinculin (fragment) P18206 + + 37 Prostaglandin-D synthase P41222 - +
I Bovine α1-Antitrypsin P12725 + + II Bovine Fetuin P12763 + + III Bovine Serotransferrin Q29443 + + IV Bovine Serum Albumin P02769 + +
§ out of the pH range shown in Figure 6
Table 1. List of proteins identified from the 2D-gels experiments
65
between the two pH values were observed. An over expression at pH 6.7 was detected
for cathepsin B and SPRC (spots 4 and 29 in Fig. 6 A), and at pH 7.5 for collagen V
α1 and collagen VI α2 (spots 10 and 11 on Fig. 6 B). Several fragments of the same
ECM protein (e.g. collagen I α1 and collagen I α2; spots 7 and 8 in Fig. 6A and B)
were sometimes observed, with differences in abundance between the two pH values.
For these proteins, indeed, the 2D-PAGE analysis may reflect a balance between
protein synthesis and degradation in the culture supernatant. The most striking
differences in protein expression were observed for agrin (undetectable at pH 7.5) and
tetranectin (undetectable at pH 6.7) (spots 3 and 31 on Fig. 6 A and Fig. 6 B,
respectively).
3.1.4. Discussion
We have performed a transcriptomic study of the temporal program of gene
expression of fibroblasts following a change of the pH of the culture medium and
serum starvation. The results obtained using NHDF as a model system have shown
that a large number of genes exhibits a modulation of expression in the experimental
conditions chosen (> 2-fold in 2,068/12,565 genes). However, a marked up- or down-
regulation of gene expression (> 5-fold) was observed only for a minority (67/12,565)
of the genes studied. For a significant proportion of these genes, the pattern of
modulated gene expression after acidification of the culture medium was different in
the presence or in the absence of serum.
For some of the genes displaying a modulated expression upon pH change, a
functional role could be postulated. For example, since fibroblasts do not proliferate
in acidic conditions and in the absence of serum, a modulation in expression of cell-
66
cycle related genes suggests an involvement of the corresponding proteins in blocking
cell division. However, a direct attribution of a physiological function to a modulated
gene is difficult, keeping in mind that levels of transcripts do not always correspond
to protein concentrations (Gygi, Rochon et al. 1999), and that physiological processes
can be regulated by mechanisms independent of protein synthesis (such as post-
translational protein modifications and/or proteolytic degradation).
As variations in the pH of tissues occur during development, in physiological and
pathological processes, the results presented in this thesis should now allow a targeted
immunohistochemical analysis of gene products whose expression is likely to be
modulated in vivo. While antibody availability remains a bottleneck for this type of
investigations, recent advances in the high-throughput in vitro production of
monoclonal antibodies may facilitate these studies in the future (Pini, Viti et al. 1998;
Elia, Silacci et al. 2002; Liu, Huang et al. 2002).
Oncofetal isoforms of modified ECM components such as TN-C and fibronectin are
characterized by a restricted expression pattern and are generated by an alternative
splicing process dependent on the intracellular pH (Carnemolla, Balza et al. 1989;
Borsi, Allemanni et al. 1996). These ECM components have been used by us and by
other groups as targets for the development of human antibody-based therapeutic
proteins (Birchler, Neri et al. 1999; Carnemolla, Castellani et al. 1999; Tarli, Balza et
al. 1999; Viti, Tarli et al. 1999; Nilsson, Kosmehl et al. 2001; Paganelli, Bartolomei et
al. 2001; Carnemolla, Borsi et al. 2002; Halin, Rondini et al. 2002; Reardon, Akabani
et al. 2002), some of which are in clinical investigations. Some of the genes identified
in our screening represent interesting candidates for possible biomedical applications.
67
For example, the extracellular matrix protein 2, whose expression is strongly up-
regulated upon serum starvation (Figure 5A and 5C), has been found to be
undetectable in most normal human tissues, with the notable exception of the uterus,
the ovaries and the visceral adipose tissue (Nishiu, Tanaka et al. 1998).
Stanniocalcin 1, the mammalian homolog of a fish calcium and phosphate
homeostasis-regulating hormone, has been shown to be highly up-regulated during
endothelial tubulogenesis (Kahn, Mehraban et al. 2000) and to be expressed both in
cancer cell lines and many types of tumors (Fujiwara, Sugita et al. 2000). In
particular, stanniocalcin 1 mRNA is localized to blood vessels in tumors (Kahn,
Mehraban et al. 2000) and its levels are dramatically enhanced in hepatocellular
carcinoma and colorectal tumors, as compared to normal tissues (Fujiwara, Sugita et
al. 2000; Gerritsen, Soriano et al. 2002). However, stanniocalcin 1 appears to be
expressed at high levels also in different adult organs, even though some
discrepancies in the literature about tissue distribution of this protein have emerged
(Ishibashi and Imai 2002).
In general, markers with a restricted pattern of expression will be the preferred choice
for the development of antibody-based biomedical strategies. However, considering
that some of the most promising tumor markers described so far are generated by
alternative splicing (the EDB domain of fibronectin, the C domain of TN-C, the H
isoform of CD44 (Carnemolla, Balza et al. 1989; Borsi, Allemanni et al. 1996;
Ohizumi, Tsunoda et al. 1998; Tsunoda, Ohizumi et al. 1999; Ohizumi, Harada et al.
2000), many potentially interesting candidate genes and/or exons may have escaped
our analysis, considering that Affymetrix technology is of only limited utility for the
study of the regulation of alternative splicing. Recently, a sensitive and specific assay
for parallel analysis of mRNA isoforms on a fiber-optic microarray platform has been
68
described. The method permits analysis of mRNA transcripts without prior RNA
purification or cDNA synthesis and is able to detect mRNA isoforms from as little as
10-100 pg of total cellular RNA or directly from a few cells (Yeakley, Fan et al.
2002).
Iyer and colleagues have studied the transcriptional program of serum-starved
fibroblasts after stimulation with serum, using cDNA microarrays (Iyer, Eisen et al.
1999). While in many aspects their study is the opposite of the serum starvation
experiment of Figure 4C, a direct comparison is hindered by the fact that different
technologies were used for the two investigations.
There is a consensus that transcriptomic investigations with the Affymetrix gene chip
system and proteomic studies with 2D-PAGE and mass spectrometry are often
complementary. While 2D-PAGE operates at the level of the genes’ functional
products, the proteins, only abundant and soluble secreted proteins with MW between
10 and 200 kDaltons could be detected in our gels. For example, the large isoform of
TN-C, which we had shown to be up-regulated at high pH at the protein level using
immunometric assays (data not shown), could not be resolved in our 2D-PAGE
experiments. Furthermore, proteomic studies of secreted proteins are also complicated
by proteolytic degradation processes and by the presence of intracellular proteins in
the culture medium, resulting from cell lysis.
One of the most striking differences between gels of fibroblasts grown at pH 6.7 and
7.5 was the relative abundance of tetranectin, a trimeric protein of 181 aminoacids
isolated from human plasma. Interestingly, tetranectin gene expression is strongly up
regulated at pH 7.5 upon serum starvation (> 5-fold; 36569_at code in Supplementary
69
Table 3 [available at the website http://www.pharma.ethz.ch/bmm/div/NHDF/]), but
is less strongly up regulated if serum removal is accompanied by acidification of the
medium. These mRNA levels correlate well with the protein abundance detected on
the 2D gels. Tetranectin is a plasminogen-binding protein that is induced during the
mineralization phase of osteogenesis and is a candidate gene for human disorders
affecting bone and connective tissue (Berglund and Petersen 1992; Wewer, Ibaraki et
al. 1994).
The results presented in this thesis provide the first genome-wide analysis of the
effects of pH and serum starvation on gene expression in fibroblasts. However, the
relevance to physiological and pathological processes of the findings observed in the
model system used (NHDF cultures) will have to be confirmed by
immunohistochemical investigations. Furthermore, efficient technologies are badly
needed for the genome-wide study of modulated patterns of alternative splicing
induced by changes of pH and/or serum starvation.
70
3.2. Modulation of gene expression by hypoxia in human umbilical cord vein
endothelial cells. A transcriptomic and proteomic study.
The reduction of oxygen supply to a given tissue below normal levels (“hypoxia”) is a
feature of many physiological and pathological conditions, including high altitude
residence, fetal development in uterus, pulmonary fibrosis, ischemia, neoplasia and
blinding ocular disorders (Helfman and Falanga 1993; Pe'er, Shweiki et al. 1995)
In cancer, the nature of tumor vasculature is one of the main reasons for the
insufficient blood delivery to the tumor mass. Tumor blood vessels are relatively
undifferentiated and leaky, often due to incompetent basement membrane (McDonald
and Baluk 2002) and lack of supporting cells. Extravasation of macromolecular
components in the stroma, together with insufficient drainage of interstitial fluids,
creates a high interstitial pressure, which is associated with corresponding low values
of intravascular pressure (Vaupel, Kallinowski et al. 1989). This impaired vascular
supply creates a gradient of nutrient and oxygen diffusion, with the result that the
tumor environment is acidic, nutrient-deprived and hypoxic. Indeed, partial pressure
of oxygen pO2 has been directly measured in human tumor xenografts by
phosphorescence quenching microscopy (Helmlinger, Yuan et al. 1997). It has been
shown that pO2 is reduced in the tumor mass, but that it can be reduced as well in
tumor blood vessels, also in the presence of an unimpaired blood flow (Helmlinger,
Yuan et al. 1997). The hypoxic stimulus triggers an adaptive response composed of a
cascade of molecular pathways, with HIF-1 and associated proteins playing a major
role in oxygen sensing within the cell (Semenza 2001). In tumors, hypoxia leads to
increased angiogenesis and glycolysis, and to alterations in micro environmental pH
71
(Goonewardene, Sowter et al. 2002). The growth of new blood vessels, stimulated at
least in part by hypoxia-induced VEGF expression (Carmeliet, Dor et al. 1998),
increases the supply of oxygen and nutrients to tumor cells and facilitates their
metastatic spread (Hanahan, Christofori et al. 1996).
Similar to solid tumors, some potentially blinding ocular disorders (including
retinopathy of prematurity, diabetic retinopathy, rubeosis iridis and the exudative
form of age-related macular degeneration) feature an over-exuberant growth of new
blood vessels, resulting in most cases from chronically reduced oxygen concentration
at the site of disease (Campochiaro 2000). Furthermore, in conditions of chronic
inflammation such as Crohn’s disease or rheumatoid arthritis, hypoxia may potentiate
ongoing tissue damage through a number of well defined pathways (Taylor and
Colgan 1999).
Macromolecular components of the cellular response to hypoxia may represent
therapeutic targets, for example for the development of low-molecular weight
inhibitors. Alternatively, several protein-based therapeutic strategies have been
postulated and tested in animal models, featuring the selective delivery of bioactive
compounds to angiogenic sites in cancer and other diseases (Birchler, Neri et al. 1999;
Carnemolla, Castellani et al. 1999; Tarli, Balza et al. 1999; Viti, Tarli et al. 1999;
Nilsson, Kosmehl et al. 2001; Carnemolla, Borsi et al. 2002; Halin, Rondini et al.
2002). These targeted strategies rely on the availability of markers of angiogenesis
(such as the ED-B domain of fibronectin (Carnemolla, Balza et al. 1989), the C-
domain of TN-C (Borsi, Allemanni et al. 1996), phosphatidyl serine (Ran, Gao et al.
1998), CD44 isoforms (Ohizumi, Tsunoda et al. 1998; Tsunoda, Ohizumi et al. 1999;
72
Ohizumi, Harada et al. 2000), VEGF receptors and their complexes (Brekken, Huang
et al. 1998), CD13 (Pasqualini, Koivunen et al. 2000), CD105 (Burrows, Derbyshire
et al. 1995) and integrins (Brooks, Montgomery et al. 1994; Friedlander, Brooks et al.
1995)), whose patterns of expression often correlate with hypoxia. The selective
targeting of these components in vivo (e.g., by means of monoclonal antibody
derivatives) may lead to more efficient anti-cancer therapy and/or to improved
imaging (Santimaria, Moscatelli et al. 2003).
Considering the role of the molecular response to hypoxia in physiological processes
and in disease, it is not surprising that major efforts are underway to characterize
genes whose expression is modulated by low oxygen concentration, with a particular
emphasis on those gene products, which can be considered as targets for
pharmaceutical intervention. Previous studies have reported the analysis of gene
expression as a response to hypoxia, using microarray technologies, in a number of
different cell types (including T84 and Caco-2 intestinal epithelial cells (Narravula
and Colgan 2001; Lee, Madan et al. 2002), PC12 pheochromocytoma cells (Seta, Kim
et al. 2001), HepG2 and HepG3 hepatoma cells (Fink, Ebbesen et al. 2001), RKO
lymphoblastoid cells (Hammond, Denko et al. 2002), A172 glyoblastoma cells
(Budanov, Shoshani et al. 2002) and PC10 pancreatic ductal carcinoma cells (Niizeki,
Kobayashi et al. 2002)), as well as in developing zebrafish embryos (Ton, Stamatiou
et al. 2002). Hypoxia-regulated genes have also been identified by
subtractive/differential mRNA analytical techniques (e.g., subtractive suppression
hybridization; (Seta, Kim et al. 2001)) and confirmed by in situ hybridization
(Budanov, Shoshani et al. 2002). Furthermore, a set of genes which are under the
73
transcriptional control of HIF-1α (the main oxygen sensor within the cells) have been
identified over the last few years (Semenza 2002).
Using the most recent Affymetrix gene chips (22,525 genes) and proteomic
techniques, we have performed a genome-wide analysis of gene expression in human
umbilical cord vein endothelial cells (HUVEC), cultured in hypoxic or normoxic
conditions. Our study confirms previous findings about hypoxic regulation of gene
expression in endothelial cells, but also identifies additional proteins and ESTs whose
expression is modulated (most often, up regulated) by a decreased oxygen
concentration. This transcriptomic analysis has been complemented by a semi-
quantitative RT-PCR analysis of patterns of expression of alternative splicing for
selected domains of extracellular matrix proteins and by a 2D-PAGE analysis of
proteins expressed by HUVEC cells, cultured in hypoxic or normoxic conditions.
3.2.1. Genome-wide analysis of gene expression modulation in HUVEC cultured in
hypoxic vs. normoxic conditions
We have performed a broad-range analysis of the modulation of gene expression in
HUVEC, as a function of time of exposure to hypoxic growth conditions, according to
the experimental scheme depicted in Figure 7. We have compared the levels of
transcripts in HUVECs grown in hypoxia at different time points (12, 24, and 48
hours), relative to control cells maintained for 48 hours in normoxic growth
conditions.
At each time point, the absolute expression levels for the more than 22,000 genes
represented in the Affymetrix Chips were measured using the Affymetrix Micro
Array Suite 2.0 software, starting from cell preparations performed in triplicate.
74
Figure 7: Experimental design. For transcriptomic experiments, replicate flasks were seeded with HUVECs and cells were grown until confluence. Monolayers were washed (t = 0) and exposed for 12, 24 or 48 hours to hypoxia (5% CO2, 2% O2) in EGM-2 medium (containing 10% FBS, pH 7.5) or exposed for 48 hours to normoxia (5% CO2, 21% O2) in the same medium. At the end of the incubation time, cells were harvested and used for total RNA extraction. For proteomic experiments, HUVECs were exposed to either hypoxia or normoxia for 48 hours, in the same conditions as described above. At the end of the incubation time, cells were harvested with a cell scraper and directly processed for 2D-PAGE analysis (whole cell lysate) or subjected to further purification procedures (cell fractionation).
The comparative analysis of gene expression was performed using the Silicon
Genetics GeneSpring version 4.2.1 software, as described in the Materials and
Methods.
3,996 out of 22,525 genes resulted to be modulated by hypoxia more than two-fold
(up-regulation or down-regulation) in at least one of the three averaged time points
(data not shown). A comprehensive list of the expression levels for these genes can be
found at http://www.pharma.ethz.ch/bmm/div/HUVEC/ as Supplementary Table 1.
75
Figure 8: Comparative analysis of HUVEC gene expression modulation as a consequence of cell exposure to hypoxia. Triplicate, independent RNA preparations were obtained from HUVEC grown for 12, 24 or 48 hours in hypoxic conditions or for 48 hours in normoxic conditions. Using the Affymetrix oligonucleotide microarray technology, a measurement of expression levels for 22,525 genes (represented on the Affymetrix HG-U133A gene chip) was carried out. Comparative analysis of gene expression levels in HUVEC exposed to hypoxia vs. normoxia was performed by GeneSpring version 4.2.1 software. Replicates were averaged, and a per gene normalization was carried out, meaning that the average values of gene expression in samples of HUVEC grown in hypoxia were used to calculate (for each gene and experimental time point) ratios relative to the average of the expression levels of the same gene in samples of HUVEC grown in normoxia for 48 hours. Sixty-five out of 22,525 genes resulted to be more than five-fold up regulated in at least one of the three time points of exposure to hypoxia (panel A). Forty-two genes resulted to be more than five-fold down regulated in at least one of the three time points of exposure to hypoxia (panel B). Figure 8A and 8B present a hierarchical clustering, according to the method of Eisen (Eisen, Spellman et al. 1998), of such 65 up-regulated and 42 down-regulated genes, respectively. Genes are grouped based on the “similarity” of their expression pattern along the experimental time dimension and are shown as horizontal bars of different colors. The similarity tree (dendrogram) is displayed in the left portion of each panel. The green-to-red color grading shown on the right-hand side of the Figure represents the ratios of gene expression levels for the different time points of HUVEC cells grown in hypoxia, relative to the corresponding gene expression levels in normoxia.
In order to reduce further the complexity of information, we performed a detailed
analysis, applying different filtering strategies. The first strategy was to focus on those
genes showing the greatest degree of modulation between the two conditions (hypoxia
and normoxia) and which were called “present” in at least one of them. Figure 8
presents a hierarchical clustering of such genes, performed by the GeneSpring
76
software according to the method of Eisen (Eisen, Spellman et al. 1998). Genes are
grouped on the basis of the “similarity” of their expression pattern along the
experimental time dimension. The similarity tree is displayed in the left portion of
each panel. A green-to-red color grading represents the ratios of gene expression
levels for the different time points of HUVEC cells grown in hypoxia, relative to the
corresponding gene expression levels in normoxic conditions. Panel A shows
hierarchical clustering of genes that resulted to be up regulated more than 5-fold (and
flagged as present) in at least one of the experimental time points (65 genes, ratio
hypoxia/normoxia > 5), while Panel B shows hierarchical clustering of genes that
resulted to be more than 5-fold down-regulated (42 genes, ratio hypoxia/normoxia
<0.2).
Fifty-two of the 65 strongly up-regulated genes were annotated genes, the rest being
ESTs and hypothetical proteins. Many of the annotated genes comprised in Fig. 8A
have already been shown to be up regulated by exposure to hypoxia, in HUVEC cells
(adrenomedullin (Ogita, Hashimoto et al. 2001), VEGFR1 (Gerber, Condorelli et al.
1997)) or in other cell model systems. Other genes were also strongly up regulated
whose expression however, to our knowledge, has never been shown to be pO2-
dependent (claudin 3, CD24, tetranectin and so on). A few genes were found to be
significantly up regulated in hypoxic growth conditions at all time points, namely the
chemokine C-X-C receptor 4, the pyruvate dehydrogenase kinase 1, the myostatin and
the taurine transporter (solute carrier family 6 member 6), many other genes were up
regulated only at late times of exposure to hypoxia (e.g., adrenomedullin, adenylate
kinase 3, VEGF, angiopoietin-like 4, EGL 9 homolog 3, stanniocalcin 1, IGFBP3).
By contrast, no gene appeared to be constantly down regulated of more than 5-fold,
but many were significantly down regulated at early times of exposure to hypoxia,
77
including clusterin, TNF superfamily member 10, collagen 1α2, small inducible
cytokine A2, and basic fibroblast growth factor (bFGF). Other genes showed rather a
down regulation later of hypoxic growth (e.g., cyclin B2, kinesin-like 1, MAD2,
nucleolar protein ANKT).
We then focused on a clustering strategy based on ontology, taking advantage of the
built-in, simplified gene ontology featured by the GeneSpring software. In Figure 9,
genes showing a modulated expression pattern, and flagged as “present” in
Affymetrix MicroArray Suite absolute analysis, were grouped into eight different
clusters according to the GeneSpring gene ontology tool: membrane proteins (Fig.
9A), ECM proteins (Fig. 9B), apoptosis regulators (Fig. 9C), cell cycle proteins (Fig.
9 D) and cell cycle regulators (Fig. 9E), receptors (Fig. 9F), ligands (Fig. 9G), and
DNA-binding proteins/transcription factors (Fig. 9H).
In hypoxia, strongly over-expressed genes belonging to ECM or ECM-related
proteins (Fig. 9 B) include collagenVα1, both procollagen proline 4-hydroxylase
alpha-polypeptides 1 and 2, lysine hydroxylase 1 and 2, matrix metalloproteinase 16.
Down-regulated genes include matrix metalloproteinases 17, stromelysin,
microfibrillar-associated protein 2 and collagen XIII α1.
At early times of exposure to hypoxic condition, the modulation of apoptosis-related
genes (Fig. 9 C) in HUVEC seems to point to an overall anti-apoptotic effect of
hypoxia that, however, is lost at later times. This pattern is mirrored in the transient up
regulation of expression of cell cycle (Fig. 9 D) genes, like CDC2, CDC20, CDC25c
and CDC6, observed at 12 hours of hypoxic growth that is followed by a significant
decrease to severely down regulated levels for almost the totality of these genes.
Similar is also the behavior of a great majority of cell-cycle related (Fig. 9 E)
78
Figure 9: Clustering of HUVEC genes modulated in hypoxic conditions. A simplified gene ontology was automatically built up by the GeneSpring software and applied to the analysis of the 3,996 genes whose expression resulted to be modulated by more than two times in up- or down regulation upon exposure of HUVEC cells to hypoxia. For a complete list of the modulated genes, see Supplementary table 1 at the website http://www.pharma.ethz.ch/bmm/div/HUVEC/). In the figure, the time course (12, 24 or 48 hours) of expression of genes coding for membrane proteins (Fig. 3A), extracellular matrix proteins (Fig. 9B), apoptosis regulators (Fig. 9C), cell cycle proteins (Fig. 9D) and cell cycle regulators (Fig. 9E), receptors (Fig. 9F), ligands (Fig. 9G), and DNA-binding proteins/transcription factors (Fig. 9H) are shown, together with the relative Affymetrix gene identifier and the gene systematic name (GeneSpring, Silicon Genetics, Redwood City, CA, USA; http://www.sigenetics.com). In the lower part of the figure, a green-to-red color grading represents the ratios of gene expression levels for the different time points of HUVEC cells grown in hypoxia, relative to the corresponding gene expression levels in normoxic HUVEC. Further information about the indicated genes, as well as the sequences of oligonucleotides employed by Affymetrix for recognition of the different genes is available at the Affymetrix website (http://www.affymetrix.com/analysis/index.affx)
79
Figure 3: Clustering of HUVEC genes modulated in hypoxic conditions (Continued).
genes, including cyclins A1, A2, B1, B2, D1, E2, F, and the cyclin-dependent kinase
inhibitor p18, which are up regulated at early times of hypoxic growth, but become
severely down regulated thereafter. A notable exception is constituted by the
80
prostacyclin synthase gene, which is moderately affected by hypoxia at early times of
exposure, but becomes dramatically up regulated at 24 and 48 hours of HUVEC
incubation in hypoxic conditions.
A few genes, among those encoding for receptors (Fig. 9 F), showed a marked
increase in their expression level in hypoxic conditions, namely FLT/VEGFR1 and
the chemokine C-X-C receptor 4 (fusin). Ligands (Fig. 9 G) which resulted to be most
prominently up regulated by hypoxia included VEGF, Del-1, TGFβ-induced 68 kDa
protein, IGFBP3 and secreted frizzled-related protein 1.
A range of genes among DNA-binding proteins/transcription factors (Fig. 9 H) were
considered as “present” in the MAS 2.0 absolute analysis, but few of them showed a
significant modulation upon shift to hypoxic growth conditions. Basic leucine zipper
transcription factor 1 appeared to be significantly up regulated up to 48 hours of
incubation in hypoxia, while STAT1 was constantly down regulated throughout all
the experimental time. The level of expression of transcripts for the hypoxia-inducible
factor 1 alpha polypeptide were not significantly modulated, in agreement with the
post-transcriptional regulation of the activity of this transcriptional activator.
Finally, we focused on the expression levels of the genes whose transcriptional
regulation is known to be under the control of the HIF transcription factor complex
(Semenza 2002), and which code for proteins involved in glycolysis and metabolism,
proliferation and survival, iron homeostasis and erythropoiesis, and angiogenesis.
Table 2 summarizes the expression level for 49 of the above genes. Thirty-nine genes
(~80%) were found to be considered present in all the experimental conditions, seven
(~14%) were always flagged “absent”. Although the majority of the present, HIF-
81
Table 2. Transcriptomic analysis of expression of HIF transcription factor complex-regulated genes 1.
Gene product GenBank Accession No.
Flag Hypoxia/normoxia
12h 24h 48h Aminopeptidase A L12468 A 0.74 0.82 1.03 Adenylate kinase 3 NM_013410 P 2.43 6.61 6.35 α1B-adrenergic receptor NM_000679 A 0.92 0.62 0.72 Adrenomedullin NM_001124 P 3.34 5.26 6.43 Aldolase A NM_000034 P 1.36 1.78 1.69 Aldolase C NM_005165 P 1.64 5.85 2.58 Carbonic anhydrase 9 NM_001216 A,P 1.04 0.89 1.17 Ceruloplasmin AA191647 P 0.68 0.92 0.77 Collagen type V α1 AI130969 P 0.70 1.33 2.26 DEC1 NM_003670 P 3.34 3.84 2.03 Endocrine gland-derived VEGF AF333022 n.a. n.a. n.a. n.a. Endothelin-1 NM_001955 P 0.83 1.01 1.35 Enolase 1 NM_001428 P 1.33 1.73 1.29 Erythropoietin NM_000799 A 0.70 0.92 1.47 ETS-1 NM_005238 P 1.81 1.38 1.06 Glucose transporter 1 NM_006516 P 1.67 2.32 2.81 Glucose transporter 3 NM_006931 P 3.70 7.28 5.67 GAPDH M33197 P 1.23 1.40 1.31 Heme oxygenase 1 NM_002133 P 0.99 0.80 0.63 Hexokinase 1 NM_000188 P 1.38 1.40 0.80 Hexokinase 2 AI761561 P 2.07 2.70 1.96 Insulin-like growth factor 2 M17863 A 0.98 1.20 0.90 IGF binding protein 1 NM_000596 P 1.62 1.68 0.55 IGF binding protein 2 NM_000597 P 0.46 0.62 1.33 IGF binding protein 3 M31159 P 1.53 2.90 10.02 Intestinal trefoil factor NM_003226 P,M,A 0.92 1.42 0.94 Lactate dehydrogenase A NM_005566 P 1.33 1.81 1.59 LDL receptor related protein 1 BF304759 A 1.30 1.27 1.79 NO synthase 2 L24553 P 1.25 1.10 1.29 NIP3 U15174 P 1.81 3.43 3.11 NIX AL132665 P 1.37 2.40 2.50 p21 NM_000389 P 1.39 1.22 0.83 p35srj AF109161 P 1.05 1.25 1.18 6-phosphofructo-2-kinase NM_004566 P 0.98 1.48 1.55 Phosphofructokinase L BC006422 P 1.23 1.77 1.54 Phosphoglycerate kinase 1 NM_000291 P 1.33 1.94 1.09 Plasminogen activator inhibitor 1 NM_000602 P 1.18 1.38 1.28 Prolyl-4-hydroxylase α(I) NM_000917 P 2.09 4.41 5.58 Pyruvate kinase M NM_002654 P 1.61 2.25 1.17 Transferrin NM_001063 A 4.36 3.93 3.90 Transferrin receptor BC001188 P 0.45 0.44 0.64 Transforming growth factor β3 J03241 A 1.15 0.99 1.35 Transglutaminase 2 M98478 P 1.05 1.14 1.07 Triosephosphate isomerase M10036 P 1.27 2.36 1.82 Vascular endothelial growth factor M27281 P 2.02 5.77 11.01 VEGF receptor FLT-1 U01134 P 5.72 9.26 3.43
[1] According to: Semenza, G., Biochem Pharmacol 2002, 64, 993-998.
82
regulated genes did not show modified expression level, a number of them (17/49,
~35%) appeared to be significantly up regulated (more than two-fold) in hypoxic
growth conditions. In HUVEC, the most striking up regulation was registered for
adenylate kinase 3, adrenomedullin, aldolase C, glucose transporter 3, IGFBP3,
procollagen prolyl 4-hydroxylase α1, VEGF and FLT-1/VEGFR1. Only transferrin
receptor gene appeared to be down regulated (about two-fold) in our experimental
conditions.
3.2.2. RT-PCR analysis of expression level and of alternative splicing for some
extracellular matrix protein genes.
Components of the modified extracellular matrix (e.g., oncofetal fibronectin and
Figure 10: Semi-quantitative RT-PCR analysis of transcript abundance and pre-mRNA alternative splicing for the extracellular matrix protein gene Del-1. (A) Total RNA was extracted from triplicate samples of HUVECs grown in hypoxia or normoxia for 48h, as described in Experimental Procedures. RNA concentration was determined and identical amounts (200 ng) of each template were used in a reverse transcription-polymerase chain reaction (RT-PCR), employing the primers Del-1for and Del-1rev (see Table 6 in Materials and Methods). N1, N2 and N3 indicate the three replicate normoxic samples; H1, H2 and H3 indicate the three replicate hypoxic samples. Arrows on the left point to the position of the two expected amplification products (110 and 80 bp for Del-1 major transcript and Del-1 Z20 splice variant, respectively). The position of relevant bands in a DNA ladder (M4) is indicated on the right. (B) A serial dilution of RNA samples N1 and H1 was realized and a total amount of 200, 20, 2 and 0.2 ng of each template (or no template at all) was employed in a RT-PCR reaction with either the Del-1for and Del-1rev primers (upper panel “Del-1”) or the GAPDHfor and GAPDHrev primers (lower panel “GAPDH”). Arrows on the left point to the position of the expected amplification products (110 and 80 bp for Del-1 major transcript and Del-1 Z20 splice variant, and 983 bp for the GAPDH transcript, respectively).
83
tenascin isoforms generated by alternative splicing of the primary transcript) have
been used successfully by our group (Birchler, Neri et al. 1999; Carnemolla,
Castellani et al. 1999; Tarli, Balza et al. 1999; Viti, Tarli et al. 1999; Nilsson,
Kosmehl et al. 2001; Carnemolla, Borsi et al. 2002; Halin, Rondini et al. 2002) and
Figure 11: Semi-quantitative RT-PCR analysis of transcript abundance and pre-mRNA alternative splicing for the extracellular matrix protein genes Fibronectin and Tenascin C. (A) A serial dilution of RNA samples N1 and H1 (see legend to Fig. 10B) was realized and a total amount of 10, 5, 2.5 and 0.125 ng of each template (or no template at all) was employed in a RT-PCR reaction with the following couples of primers (see Table 6 in Materials and Methods for details): the FN-ED/Bfor and FN-ED/Brev primers (panel A: “FN ED/B”); the FN-ED/Afor and FN-ED/Arev primers (panel A: “FN ED/A”); the FN1for and FN1rev primers (panel A: “FN type III 2-4”) or the GAPDHfor and GAPDHrev primers (panel A: “GAPDH”). Arrows on the right point to the position of the expected amplification products (268 bp for the fibronectin extra-domain B, 263 bp for the fibronectin extra-domain A, 999 bp for the fibronectin type III homology domains 2-4, and 983 bp for the GAPDH transcript, respectively). The position of relevant bands in a DNA ladder (M4) is shown. (B) The same serial dilution of RNA samples N1 and H1 (see legend to Fig. 10B) was realized and a total amount of 10, 5, 2.5, 0.125 and 0,062 ng of each template (or no template at all; not shown) was employed in a RT-PCR reaction with the following couples of primers (see Table 6 for details): the TNCCfor and TNCCrev primers (panel B: “TNC repeat C”); the TNCDfor and TNCDrev primers (panel B: “TNC repeat D”); or the TNCfor and TNCrev primers (panel B: “TNC FN III 6-7). Arrows on the right point to the position of the expected amplification products (269 bp for the tenascin C repeat C, 263 bp for the tenascin C repeat D, and 368 bp for the tenascin C FN-type III repeats 6-7, respectively). The position of relevant bands in a DNA ladder (M4) is shown.
84
others (Paganelli, Bartolomei et al. 2001; Reardon, Akabani et al. 2002) as targets for
ligand-based anti-cancer intervention. We confirmed the hypoxia-induced over
expression of Del-1 (Figure 3) using semi-quantitative RT-PCR (Figure 10 A and 10
B). Del-1 has been reported as an endothelial cell-specific gene, absent in all adult
tissues tested, but strongly over expressed in certain fetal tissues and in a number of
tumors (Hidai, Zupancic et al. 1998; Aoka, Johnson et al. 2002). No significant up
regulation of fibronectin isoforms containing the extra-domains EDA and EDB could
be detected by RT-PCR (Fig. 11 A). These isoforms are known to be expressed by
proliferating ECs in aggressive tumors such as glioblastoma (Castellani, Borsi et al.
2002). Similarly, no striking over expression of tenascin-C isoforms could be detected
(Fig. 11 B), consistent with the observation that these ECM components are mainly
expressed by stromal cells and cancer cells in tumors (Hindermann, Berndt et al.
1999).
3.2.3. Proteomic study
We have used 2D-PAGE to study patterns of protein expression in HUVEC exposed
to hypoxic (2 % O2) or normoxic conditions (21 % O2). Figure 12 shows comparative
2D-gels of cell lysates. The vast majority of proteins showed comparable expression
levels of different samples exposed either to hypoxic or normoxic conditions, with the
possible exceptions of the heterogeneous nuclear ribonucleoprotein K and the 60 kDa
heat shock protein mitochondrial precursor (see Figure 12, spot 11 and Table 3) and
the 60 kDa heat shock mitochondrial precursor (Fig. 12, spot 14 and Table 3), which
were up regulated in hypoxic and normoxic conditions respectively. A 2D-PAGE
reference map of proteins contained in the HUVEC cell lysate is available at the
website http://www.pharma.ethz.ch/bmm/div/HUVEC/.
85
Figure 12: 2D-PAGE of a whole cell lysate of HUVEC grown in hypoxic and normoxic conditions. Proteins contained in whole cell lysates of HUVEC cells, grown for 48 hours in normoxic (gel “Normoxia”) or hypoxic (gel “Hypoxia”) conditions, were separated on 2D-PAGE gels, as described in Experimental Procedures. Two representative gels are shown, in which the IPG strips pH range was 3-10. Molecular weight markers were included and their values are indicated on the left. Two selected areas (a and b) in which differences in protein expression patterns could be appreciated are shown, on a magnified scale and in duplicate, at the bottom of each respective 2D-gel (N1a and N2a, N1b and N2b: replicates of normoxic HUVEC samples, magnification of areas a and b; H1a and H2a, H1b and H2b: replicates of hypoxic HUVEC samples, magnification of areas a and b). All the spots indicated by a number were excised from the gel, trypsin-digested and subjected to mass spectrometric analysis. A circled number identifies a protein over-expressed in hypoxia, a number in a square indicates a protein repressed in hypoxia. The correspondence between spot numbering and protein identification is given in Table 3.
86
Table 3 . Protein identification in whole cell lysate of HUVEC grown in normoxic and hypoxic conditions.
ID
Common name
SwissProt
Accession
Sequence coverage
%
pI (theor.)
pI (exptl.)
MW (theor.)
MW (exptl.) Normoxia Hypoxia
Ratio
H/N
1 Heat shock 27 kDa protein (HSP 27)
P04792 13.6 5.98 5.60 22782 23802 0.61 ± 0.17
0.19 ± 0.00 0.31
2 Endoplasmin precursor
P14625 6.0 4.76 4.58 92468 97335 4.78 ± 0.12
7.16 ± 1.19 1.50
3 Alpha-actinin 1
Alpha-actinin 4
P12814
O43707
4.6
6.3
5.22
5.27
5.28
5.17
102974
104854
103780
104383 1.44 ± 0.27
1.77 ± 0.36 1.22
Heat shock protein HSP 90-beta
P08238 9.5 4.97 83133
4 Transitional endoplasmic reticulum ATPase
P55072 4.2 5.14
4.81
89322
88018 4.55 ± 0.37
5.00 ± 1.41 1.10
5 78 kDa Glucose-regulated protein precursor (GRP 78)
P11021 22.9 5.07 4.82-86 72333 79307 4.42 ±
0.71 4.83 ± 0.49 1.09
6 Lamin B1 P20700 4.1 5.11 5.03 66277 73864 0.78 ± 0.19
0.95 ± 0.04 1.22
7 Lamin B1 P20700 9.2 5.11 5.09 66277 73853 0.68 ± 0.22
1.15 ± 0.03 1.68
8 Heat shock 70 kDa / 71 kDa protein
P08107 6.5 5.48 5.25 70052 75790 2.79 ± 0.53
2.67 ± 0.05 0.96
9 Heat shock 70 kDa / 71 kDa protein
P08107 10.5 5.48 5.34 70052 75687 4.84 ± 0.77
4.37 ± 0.19 0.90
10 Vimentin P08670 46.1 5.06 4.94 53554 70290 23.50 ± 0.49
27.14 ± 2.94 1.15
11
Heterogeneous nuclear ribonucleoprotein K
60 kDa Heat shock protein mitochondrial precursor
Q07244
P10809
4.3
6.5
5.39
5.70 5.12
50976
61054
70703 1.37 ± 0.26
2.59 ± 0.78
1.89
12 Vimentin
Tubulin alpha 1
P086701
P05209
32.2
30.8
5.06
4.94 4.94
53554
50151 64656 20.09 ±
0.18 23.38 ±
2.63 1.16
13 60 kDa Heat shock protein mitochondrial precursor
P10809 9.1 5.70 5.11 61054 68560 1.30 ± 0.21
1.238 ± 0.17 0.94
14 60 kDa Heat shock protein mitochondrial precursor
P10809 14.0 5.70 5.20 61054 67860 3.73 ± 0.82
2.30 ± 0.35 0.62
15 T-complex protein 1, epsilon subunit (TCP-1-epsilon)
P48643 2.2 5.45 5.52 59671 68761 1.12 ± 0.06
1.25 ± 0.35 1.12
87
Table 3 . Protein identification in whole cell lysate of HUVEC grown in normoxic and hypoxic conditions. Continued
ID
Common name
SwissProt
Accession
Sequence coverage
%
pI (theor.)
pI (exptl.)
MW (theor.)
MW (exptl.) Normoxia Hypoxia
Ratio
H/N
16 Protein disulfide-isomerase A3
P30101 14.9 5.98 5.95 56782 60928 3.73 ± 0.45
4.94 ± 0.59 1.32
17 Vinculin P18206 2.4 5.51 6.52 123668 115665 0.62 ± 0.02
0. 63 ± 0.08 1.01
18 Dihydropyrimidinase related protein 2
Q16595 5.8 5.95 6.79 62293 69015 1.17 ± 0.20
1.29 ± 0.16 1.11
19 Heterogeneous nuclear ribonucleoprotein H1
P31943 3.8 5.89 6.30 49229 59542 1.81 ± 0.20
2.24 ± 0.15 1.24
20 T-complex protein 1, beta subunit (TCP-1-beta)
P78371 17.0 6.01 6.71 57488 60685 1.24 ± 0.25
1.42 ± 0.31
1.14
21 Elongation factor Tu P49411 6.5 7.26 7.24 49541 46482 1.08 ± 0.23
0.92 ± 0.03 0.85
22 Glutathione S-transferase P P09211 13.4 5.44 5.56 23224 23034 2.27 ±
0.44 2.68 ± 0.09 1.18
23 Ubiquitin carboxyl-terminal hydrolase isozyme
P09936 6.5 5.33 5.37 24824 23351 1.24 ± 0.17
1.33 ± 0.03 1.07
24 14-3-3 protein zeta-delta P29312 10.1 4.73 4.45 27745 26453 1.19 ±
0.06 2.62 ± 1.99 2.20
25 L-lactate hydrogenase B chain P07195 21.6 5.72 5.90 36507 35123 1.71 ±
0.10 1.50 ± 0.05 0.88
26 Protein disulfide isomerase A6 precursor
Q15084 11.1 4.95 4.91 48121 54025 2.64 ± 0.27
2.63 ± 0.19 0.99
27 ATP synthase beta chain, mitochondrial P06576 21.0 5.26 4.85 56560 58398 2.92 ±
0.18 2.61 ± 0.11 0.89
28 ATP synthase alpha chain, mitochondrial P25705 14.5 9.16 8.08 59750 54170 1.60 ±
0.51 1.21 ± 0.03 0.75
29 Pyruvate kinase, M1 isozyme P14618 10.0 7.95 7.98 57805 59328 3.05 ±
0.30 4.06 ± 1.35 1.33
30 Glucosidase II, alpha subunit Q9P0X0 5.4 5.82 6.12 109438 107334 1.35 ±
0.06 1.67 ± 0.06 1.24
31 Ubiquitin-activating enzyme E1 P22314 1.4 5.49 5.39 117849 110345 0.77 ±
0.05 0.44 ± 0.12 0.57
32 Elongation factor 1 gamma P26641 12.6 6.25 6.86 50119 59467 2.00 ±
0.47 2.41 ± 0.14 1.20
33 Chloride intracellular channel
Protein 1 O00299 11.2 5.09 4.99 26923 28327 1.62 ±
0.16 1.83 ± 0.12 1.13
88
Table 3 . Protein identification in whole cell lysate of HUVEC grown in normoxic and hypoxic conditions. Continued
ID
Common name
SwissProt
Accession
Sequence coverage
%
pI (theor.)
pI (exptl.)
MW (theor.)
MW (exptl.) Normoxia Hypoxia
Ratio
H/N
34 Thioredoxin domain containing protein 5 Q8NBS9 16.4 5.63 5.18 47629 53628 2.06 ±
0.12 2.19 ± 0.01 1.06
35 Eukaryotic initiation factor 4A2 Q14240 13.2 5.33 5.30 46489 49365 2.88 ±
0.37 2.51 ± 0.88 0.87
36 Actin, cytoplasmic 1 P02570 25.1 5.29 5.17 41736 47325 8.77 ± 2.37
12.22 ± 0.67 1.39
37 Tropomyosin alpha 3 chain P06753 33.1 4.68 4.43 32819 31019 3.18 ±
1.06 6.22 ± 0.32 1.95
38 Annexin V P08758 26.0 4.94 4.71 35805 31334 3.27 ± 0.50
4.26 ± 0.47 1.30
39 Prohibitin P35232 21.7 5.57 5.50 29804 27092 1.13 ± 0.04
0.99 ± 0.15 0.87
40 Heat shock 27 kDa protein P04792 43.4 5.98 6.34 22782 24295 1.20 ±
0.14 0.84 ± 0.00 0.70
41 Annexin A2 P07355 47.8 7.56 7.80 38473 34871 5.25 ± 0.26
7.27 ± 1.74 1.38
Table 3 The table lists the proteins identified in whole cell lysates of HUVEC grown in normoxic or hypoxic conditions, shown in Fig. 12. A software-aided quantization of spot intensities in normoxic and hypoxic samples, as well as the ratio of hypoxic vs. normoxic spot intensities, the experimental isoelectric point and molecular weight are provided for each identified protein. Theoretical isoelectric point and molecular weight are derived from the amino acidic sequences published under the different SwissProt accession numbers and refer to the unprocessed precursor.
Figure 13: Experimental design of the cell fractionation proteomic experiment. For the cell fractionation, triplicate, independent samples were prepared from normoxic and hypoxic HUVEC cells. The cells were detached with a scraper, swollen for 20 minutes and lysed in a hypotonic buffer. The cells were then spun for 15 minutes at 3,000 g and the resulting pellet (Pellet 1) was washed extensively with hypotonic buffer before being subjected to 2D-PAGE analysis. The supernatant of this low-speed centrifugation (Sup 1) was further spun for 2 hours at 100’000xg at 2°C to pellet the microsomal fraction (Pellet 2). This pellet was washed extensively with hypotonic buffer and analyzed by 1D SDS-PAGE (data not shown). The proteins in the ultracentrifugation supernatant (Sup 2) were precipitated with trichloroacetic acid, redissolved in 100 µl of rehydration solution and subjected to 2D-PAGE analysis (data not shown).
89
Figure 14: 2D-PAGE of the cell fractionation “Pellet 1” in HUVEC grown in hypoxic and normoxic conditions. Proteins contained in “Pellet 1” of the cell fractionation protocol (see Experimental Procedures and legend to Fig. 7) of HUVEC cells, grown for 48 hours in normoxic (gel “Normoxia”) or hypoxic (gel “Hypoxia”) conditions, were separated on 2D-PAGE gels, as described in Experimental Procedures. Two representative gels are shown, in which the IPG strips pH range was 3-10. Molecular weight markers were included and their values are indicated on the left. Two selected areas (a and b) in which differences in protein expression patterns could be appreciated are shown, on a magnified scale and in triplicate, at the bottom of each respective 2D-gel (N1a, N2a and N3a, N1b, N2b and N3b: triplicates of normoxic HUVEC samples, magnification of areas a and b; H1a H2a and H3a, H1b, H2b and H3b: triplicates of hypoxic HUVEC samples, magnification of areas a and b). All the spots indicated by a number were excised from the gel, trypsin-digested and subjected to mass spectrometric analysis. A circled number identifies a protein over-expressed in hypoxia. The correspondence between spot numbering and protein identification is given in Table 4.
90
Table 4 . Protein identification in cytoplasmic fraction of HUVEC grown in normoxic and hypoxic conditions
ID Common name
SwissProt
Accession
Sequence coverage
%
pI (theor.)
pI (exptl.)
MW (theor.)
MW (exptl.)
Normoxia
(X±SD)
Hypoxia
(X±SD)
Ratio
H/N
1
Procollagen-lysine, 2-oxoglutarate 5-dioxygenase
O00469 1.9 6.15 6.58 84663 94046 0.15 ± 0.07
0.37 ± 0.26 2.45
2
Procollagen-lysine, 2-oxoglutarate 5-dioxygenase
O00469 3.5 6.15 6.70 84663 93901 0.22 ± 0.01
0.48 ± 0.35 2.20
Procollagen-lysine, 2-oxoglutarate 5-dioxygenase
O00469 1.9 6.15 84663 3
Annexin A1 P04083 11.8 6.64
6.85
38583
93613 0.26 ± 0.02
0.47 ± 0.29 1.76
4 Alpha-enolase P06733 17.1 6.99 6.98 47037 51685 0.69 ± 0.30
1.48 ± 0.33 2.14
5 Alpha-enolase P06733 17.7 6.99 7.24 47037 51160 0.49± 0.18
1.31 ± 0.03 2.64
6 EBNA-2 co-activator (100kD)
Q13122 4.5 6.62 7.57 99691 98111 0.61 ± 0.03
0.24 ± 0.03 0.40
Desmoplakin I P15924 6.0 6.44 331775
Plakoglobin P14923 7.1 5.95 81498
Actin,
cytoplasmic 1 P02570 9.1 5.29 41736
Desmoglein type 1 Q02413 1.8 4.90 113715
7
EBNA-2 co-activator (100kD)
Q13122 5.1 6.62
7.64
99691
97799 0.80 ± 0.09
0.31 ± 0.07 0.38
8
ATP-dependent DNA helicase II, 70 kDa subunit
P12956 9.2 6.23 7.04 69711 72781 0.95 ± 0.07
0.54 ± 0.20 0.56
9
Chaperonin containing t-complex polypeptide 1, beta subunit
P78371 17.0 6.01 6.71 57488 60865 1.55 ± 0.07
1.11 ± 0.17 0.74
91
Table 4 . Protein identification in cytoplasmic fraction of HUVEC grown in normoxic and hypoxic conditions Continued
ID Common name
SwissProt
Accession
Sequence coverage
%
pI (theor.)
pI (exptl.)
MW (theor.)
MW (exptl.)
Normoxia
(X±SD)
Hypoxia
(X±SD)
Ratio
H/N
10 T-complex protein 1, zeta subunit
P40227 5.3 6.24 7.02 58024 64903 1.34 ± 0.47
1.00 ± 0.18 0.75
11
Tu translation elongation factor, mitochondrial
P49411 16.8 7.26 7.24 49541 46482 1.17 ± 0.23
0.89 ± 0.15 0.76
12 Alpha-enolase P06733 14.3 6.99 7.50 47037 50771 0.52 ± 0.05
1.09 ± 0.82 2.10
13 Heat shock 60kDa protein 1 P10809 30.0 5.70 5.20 61054 67860 5.42 ±
0.40 4.32 ± 1.39 0.79
14 Heat shock 60kDa protein 1 P10809 25.8 5.70 5.11 61054 68560 2.08 ±
0.47 1.51 ± 0.51 0.73
15 Major vault protein Q14764 7.3 5.34 5.39 99327 110345 0.77 ±
0.05 0.44 ± 0.12 0.57
16 Annexin V P08758 26.0 4.94 4.71 35805 31334 5.21 ± 0.51
4.93 ± 0.72 0.94
17
Stress-70 protein, mitochondrial precursor (75 kDa glucose regulated protein)
P38646 12.8 5.87 5.43 73680 78108 1.11 ± 0.64
0.95 ± 0.41 0.85
18
Inner membrane protein, mitochondrial (Mitofilin)
Q16891 8.8 6.08 6.31 83678 88108 0.46 ± 0.06
0.32 ± 0.03 0.70
19
Protein disulfide isomerase A3 precursor
P30101 28.1 5.98 5.95 56782 60928 4.23 ± 1.07
4.53 ± 1.25 1.07
20
Protein disulfide isomerase A3 precursor
P30101 30.9 5.98 5.75 56782 61084 5.44 ± 1.56
4.75 ± 0.64 0.87
21
Protein disulfide isomerase A3 precursor
P30101 17.6 5.98 5.64 56782 61163 3.11 ± 1.38
3.86 ± 2.19 1.24
22 Annexin A2 P07355 47.8 7.56 7.80 38473 34871 6.75 ± 1.30
6.79 ± 1.63 1.01
23 Annexin A2 P07355 29.5 7.56 7.79 38473 36038 2.08 ± 0.40
1.95 ± 0.70 0.93
92
Table 4 . Protein identification in cytoplasmic fraction of HUVEC grown in normoxic and hypoxic conditions Continued
ID Common name
SwissProt
Accession
Sequence coverage
%
pI (theor.)
pI (exptl.)
MW (theor.)
MW (exptl.)
Normoxia
(X±SD)
Hypoxia
(X±SD)
Ratio
H/N
24 Thioredoxin domain containing protein 5
Q8NBS9 16.4 5.63 5.18 47629 53628 2.12 ± 0.90
1.82 ± 0.82 0.86
25
Thioredoxin domain containing protein 5
Q8NBS9 8.6 5.63 5.10 47629 53628 2.36 ± 1.29
2.19 ± 0.77 0.93
26
Protein disulfide isomerase A6 precursor
Q15084 26.8 4.95 5.02 48121 53970 3.02 ± 0.82
3.75 ± 0.62 1.24
27 Lamin B1 P20700 10.8 5.11 5.09 66277 73853 1.03 ± 0.58
1.09 ± 0.39 1.06
28 Vimentin P08670 8.2 5.06 4.94 53554 64656 1.84 ± 0.41
0.84 ± 0.53 0.46
Q96EA8 13.2 5.33 46489
29
Similar to eukaryotic translation initiation factor 4A2
Transcription
factor NF-AT
45K
Q12905 11.1 8.26 5.30
44697 49365 2.21 ±
0.14 1.93 ± 0.61 0.87
30 Glutathione Transferase P1/1
P09211 32.5 5.44 5.74 23224 23549 0.99 ± 0.15
1.15 ± 0.19 1.16
Enoyl-CoA hydratase P30084 17.9 8.34 31371
31 Heat shock 27 kDa protein P04792 34.1 5.98
6.34
22782
24295 1.06 ± 0.29
1.45 ± 0.53 1.37
32
Guanine nucleotide binding protein beta subunit 2
P11016 15.1 5.60 7.80 37331 28441 1.44 ± 0.06
1.48 ± 0.53 1.03
33
Voltage-dependent anion-selective channel protein 2
P45880 15.0 6.32 7.73 38093 30886 0.68 ± 0.21
1.02 ± 0.34 1.50
34 Lamin A/C P02545 8.7 6.57 7.47 74139 75174 0.63 ± 0.27
0.71 ± 0.46 1.14
35 Heat shock cognate 71 kDa protein
P11142 12.2 5.37 5.17 70898 75893 1.08 ± 0.40
0.86 ± 0.26 0.79
93
Table 4 . Protein identification in cytoplasmic fraction of HUVEC grown in normoxic and hypoxic conditions Continued
ID Common name
SwissProt
Accession
Sequence coverage
%
pI (theor.)
pI (exptl.)
MW (theor.)
MW (exptl.)
Normoxia
(X±SD)
Hypoxia
(X±SD)
Ratio
H/N
36 Vimentin P08670 3.4 5.06 4.60 53554 52950 2.10 ± 1.16
1.43 ± 1.14 0.68
37 Tropomyosin alpha 4 chain P07226 20.4 4.67 4.36 28522 30885 3.47 ±
0.70 3.30 ± 1.42 0.95
38 Tropomyosin alpha 3 chain P06753 33.1 4.68 4.43 32819 31019 3.29 ±
0.37 3.93 ± 1.65 1.20
P35232 21.7 5.57 29804
Q9HAP4 7.4 5.67 5.50 43475 27092
39
Prohibitin
Leucine-zipper protein FKSG13
Vacuolar ATP synthase subunit B, brain isoform
P21281 7.2 5.57 56501
1.92 ± 0.22
1.41 ± 0.30 0.73
P02570 25.1 5.29 41736
P02568 24.1 5.23 5.22 42051 47751
40
Actin, cytoplasmic 1
Alpha-actin 1
Mirror-image
polydactyly
protein Q8TD10 6.3 5.55 51537
6.99 ± 0.49
6.70 ± 0.41 0.96
Actin, cytoplasmic 1 P02570 25.0 5.29 41736
41
Alpha-actin 1 P02568 23.9 5.23
5.17
42051
47325 7.68 ± 1.10
7.30 ± 0.46 0.95
42 Endoplasmin precursor P14625 8.8 4.76 4.58 92468 97335 4.72 ±
2.04 5.37 ± 1.23 1.14
43 Heat shock protein HSP 90-beta
P08238 6.9 4.97 4.81 83133 88018 3.99 ± 2.18
3.66 ± 0.62 0.92
44
78 kDa glucose-regulated protein precursor
P11021 34.6 5.07 4.84 72333 79307 12.42 ± 2.94
11.28 ± 1.84 0.91
45 14-3-3 protein zeta/delta P29312 12.2 4.73 5.25 27745 75790 2.88 ±
0.75 2.42 ± 0.46 0.84
46 Heat shock 70/71 kDa protein
P08107 15.7 5.48 5.34 70052 75687 5.03 ± 0.47
3.88 ± 0.21 0.77
47 Annexin A2 P07355 34.8 7.56 5.54 38473 75277 2.36 ± 0.24
1.87 ± 0.22 0.79
48 Annexin A2 P07355 11.2 7.56 5.53 38473 77894 2.51 ± 0.54
1.97 ± 0.47 0.79
49 Annexin A2 P07355 34.8 7.56 5.13 38473 95516 2.86 ± 0.12
2.41 ± 0.41 0.84
94
Table 4 . Protein identification in cytoplasmic fraction of HUVEC grown in normoxic and hypoxic conditions Continued
ID Common name
SwissProt
Accession
Sequence coverage
%
pI (theor.)
MW (theor.)
MW (exptl.)
Normoxia
(X±SD)
Hypoxia
(X±SD)
Ratio pI (exptl.) H/N
50 Actin, cytoplasmic 1 P02570 14.9 5.29 5.17 41736 104383 0.78 ±
0.03 0.76 ± 0.13 0.97
51 Actin, cytoplasmic 1 P02570 18.9 5.29 5.23 41736 104043 1.43 ±
0.17 1.28 ± 0.39 0.90
52 Actin, cytoplasmic 1 P02570 17.9 5.29 5.28 41736 103780 1.098 ±
0.20 1.27 ± 0.20 1.17
53 Triosephosphate isomerase P00938 12.9 6.51 4.53 26538 66276 7.92 ±
1.32 8.75 ± 3.56 1.10
54
Translation elongation factor eEF-1 gamma chain
P26641 5.9 6.25 4.49 50119 44259 2.14 ± 0.68
2.15 ± 0.52 1.00
55 Peroxiredoxin 1 Q06830 37.7 8.27 4.30 22110 29344 1.48 ± 0.77
1.49 ± 0.74 1.00
56 Peroxiredoxin 1 Q06830 32.7 8.27 5.50 22110 34012 1.76 ± 0.77
1.65 ± 0.70 0.94
57 Peroxiredoxin 1 Q06830 22.1 8.27 5.50 22110 34718 2.13 ± 0.40
2.00 ± 0.51 0.94
58 Peroxiredoxin 1 Q06830 37.2 8.27 7.50 22110 35306 2.32 ± 0.29
2.42 ± 0.19 1.04
Table 4 The table lists the proteins identified in cell fractionation “Pellet 1” of HUVEC grown in normoxic or hypoxic conditions, and shown in Fig. 14. A software-aided quantization of spot intensities in normoxic and hypoxic samples, as well as the ratio of hypoxic vs. normoxic spot intensities, the experimental isoelectric point and molecular weight are provided for each identified protein. Theoretical isoelectric point and molecular weight are derived from the amino acidic sequences published under the different SwissProt accession numbers and refer to the unprocessed precursor.
In order to get a better insight about the presence of differentially expressed proteins,
we performed a protein fractionation according to scheme of Figure 13. Figure 14
shows gels run with proteins of “Pellet 1”. Again, while most proteins exhibited
similar patterns of expression, an up regulation for procollagen-lysine 2-oxoglutarate
5-dioxygenase and α-enolase could be detected in hypoxic conditions. The most
striking observation of the study was the extremely small number of differentially
expressed proteins detected. The quantitative results of this analysis are reported in
Table 4. Proteins known to be present at different levels in normoxic and hypoxic
95
conditions (e.g., HIF-1α, PHD2 and PHD3 (Cioffi, Liu et al. 2003) escaped detection,
possibly due to relatively low abundance.
3.2.4. Discussion
In this thesis, we report a broad-range analysis, performed by means of
oligonucleotide microarray analysis, semi-quantitative RT-PCR analysis and 2D-
PAGE/tandem mass spectrometry, of the modulation of gene expression and protein
production in vascular endothelial cells exposed to hypoxia.
Although previous papers have dealt with the transcriptional regulation of distinct
genes in endothelial cells exposed to hypoxia (Ogawa, Gerlach et al. 1990; Ogawa,
Clauss et al. 1991), or recently with the transcriptional response of VEGF-treated
endothelial cells (Wary, Thakker et al. 2003), this report is, to our knowledge, the first
attempt to provide a comprehensive list of genes and proteins whose expression and
production, respectively, are modulated by hypoxic conditions in vascular endothelial
cells.
The relevance of the chosen model to the real situation in the tumor environment may
appear not immediately evident. One might argue that, being continuously exposed to
the blood stream, vascular endothelial cells in tumor blood vessels are less likely to be
exposed to hypoxia, than tumor cells in regions distant from blood vessels are.
However, experimental data show that this is not the case. By using fluorescence ratio
imaging and phosphorescence quenching microscopy, Helmlinger and colleagues
have published a comprehensive investigation of profiles of pH and oxygen
concentrations in different human tumor xenografts, implanted in transparent dorsal
chambers in SCID mice (Helmlinger, Yuan et al. 1997). The authors showed that pO2
values in the tumor mass average 8.3 ± 1.6 mmHg (with a substantial reduction with
96
respect to normal tissue values in which pO2 averages 20-40 mmHg), with minimum
values dropping to less than 0.5 mmHg. They also showed that pO2 values could be
reduced in tumor blood vessels as well, and this also in the presence of an unimpaired
blood flow (Helmlinger, Yuan et al. 1997). Therefore, vascular endothelial cells in
tumors are de facto exposed to hypoxia and therefore may contribute to the onset of
an angiogenic response.
3.2.4.1. Transcriptomics
Our transcriptomic analysis shows that the expression of about 4,000 genes is
modulated by more than two times in up- or down-regulation in hypoxic HUVEC,
with respect to the normoxic control, in at least one of the time points examined.
Sixty-five genes are strongly up regulated (> 5 times in at least one time point, see
Fig. 8A), forty-two genes are markedly down regulated (>5 times in at least one time
point, see Fig. 8B), many other genes are also significantly up-regulated or down
regulated, even though to a lesser extent (see Supplementary Table 1,
http://www.pharma.ethz.ch/bmm/div/HUVEC/].
Many of the identified genes are relevant to the cell’s adaptive response to reduced
pO2 in the environment, mainly controlled by the HIF-1 transcriptional activator
complex and its regulators (Semenza 2002). Their list include, but it is not limited to,
both VEGF A and VEGF C as well as FLT/VEGFR1 and the HIF prolyl hydroxylases
PHD2 and PHD3 (Semenza 2001). Neither HIF-1α nor HIF-1β genes appear to be
modulated at a transcriptional level. The asparagyl hydroxylase FIH-1 (factor
inhibiting HIF-1, (Mahon, Hirota et al. 2001)) gene is also not up-regulated by
hypoxia in HUVEC cells. Other up-regulated transcripts encode for proteins involved
97
in controlling cellular metabolism (Iyer, Kotch et al. 1998) and include enzymes of
the glycolytic pathway (hexokinase 2, aldolase C, triosephosphate isomerase,
phosphoglucomutase, enolase gamma, pyruvate kinase M and pyruvate
dehydrogenase kinase 1) and glucose transporters (glucose transporters 1 and 3).
Other glycolytic enzyme transcripts were not modulated or weakly up regulated
(glyceraldehyde phosphate dehydrogenase, enolase 1)
In agreement with previous data of the literature (Le Jan, Amy et al. 2003), hypoxia
up-regulates the expression of angiopoietin-like 4 gene in HUVEC cells.
Angiopoietin-like 4, originally identified as a peroxisome proliferator-activated
receptor alpha and gamma target gene (Yoon, Chickering et al. 2000), induces in the
chicken chorioallantoic membrane assay a strong proangiogenic response,
independently of vascular endothelial growth factor. In human pathology,
angiopoietin-like 4 mRNA is produced in ischemic tissues (Le Jan, Amy et al. 2003),
in conditions such as critical leg ischemia, while in tumors it is produced in the
hypoxic areas surrounding necrotic regions (Le Jan, Amy et al. 2003). It is worth
noting that, differently from data reported in hypoxic cardiomyocytes (Belanger, Lu et
al. 2002), hypoxia exposure of HUVEC cells results in a strong and sustained up-
regulation of peroxisome proliferator-activated receptor gamma.
The chemokine receptor CXCR4 mRNA expression is strongly up regulated in
HUVEC cells at all time points of hypoxic exposure (see Fig. 8A). Indeed, CXCR4
has been very recently shown to be up regulated by hypoxia in a variety of cell types,
including HUVEC (Schioppa, Uranchimeg et al. 2003) and to be regulated by the Von
Hippel-Lindau tumor suppressor pVHL (Staller, Sulitkova et al. 2003). The data of
98
this last paper suggest a possible involvement of CXCR4 in the acquisition by tumor
cells of the ability to metastasize to specific secondary sites and show a strong
association between cell surface expression of CXCR4 receptor and poor prognosis in
clear cell renal cell carcinoma patients (Staller, Sulitkova et al. 2003) . It has also
been shown that exposure to VEGF of human brain microvascular endothelial cells
results in up-regulation of CXCR4, of the monocyte chemoattractant protein MCP-1,
and of the chemokine IL-8. Our results confirm that exposure to hypoxia of HUVECs
strongly induces CXCR4 mRNA expression, while it results in a strong down
regulation of MCP-1 and IL-8 transcript levels.
Stanniocalcin 1, the mammalian homolog of a fish calcium and phosphate
homeostasis-regulating hormone, has been shown to be highly up-regulated during
endothelial tubulogenesis (Kahn, Mehraban et al. 2000) and to be expressed both in
cancer cell lines and many types of tumors (Fujiwara, Sugita et al. 2000). In
particular, stanniocalcin 1 mRNA is localized to blood vessels in tumors (Kahn,
Mehraban et al. 2000) and its levels are dramatically enhanced in hepatocellular
carcinoma and colorectal tumors, as compared to normal tissues (Fujiwara, Sugita et
al. 2000; Gerritsen, Soriano et al. 2002) and in many tumor cell lines upon exposure
to hypoxia (Lal, Peters et al. 2001). However, stanniocalcin 1 appears to be expressed
at high levels also in different adult organs, even though some discrepancies in the
literature about tissue distribution of this protein have emerged (Ishibashi and Imai
2002).
The list of transcripts whose expression resulted to be strongly up-regulated by
exposure to hypoxic growth conditions in HUVECs (Fig. 8A) comprises several other
99
genes that have already been identified as being directly controlled by the
transcriptional activator HIF-1 (adenylate kinase 3 (Wood, Wiesener et al. 1998),
adrenomedullin (Cormier-Regard, Nguyen et al. 1998), procollagen proline 4-
hydroxylase (Takahashi, Takahashi et al. 2000), insulin growth factor-binding protein
3 (Feldser, Agani et al. 1999)) and other genes for which an increased expression in
hypoxia-related conditions in vitro or in vivo has been already reported (lysyl oxidase
(Brody, Kagan et al. 1979), taurine transporter (Saransaari and Oja 1999; Schaffer,
Pastukh et al. 2002), stanniocalcin (Lal, Peters et al. 2001), N-myc downstream
regulated (Park, Adams et al. 2000), inhibin beta A (Lai, Sirimanne et al. 1996)).
However, the list includes also many other genes and ESTs (16 annotated genes, 8
cDNA clones, 4 hypothetical proteins) for which an up-regulation in mRNA
expression upon exposure to hypoxia, to our knowledge, has never been reported
before. Among these genes, claudin 3, CD24 and tetranectin deserve some comment.
Claudin 3 is a member of a family of integral membrane proteins, the claudins, which,
together with occludins, represent the major constituents of tight junctions (Morita,
Furuse et al. 1999), critical structures for the maintenance of cellular polarity, as well
as for the establishment of a permeability barrier for paracellular transport in epithelial
and endothelial cells (Tsukita, Furuse et al. 2001). A strong up-regulation of claudin-3
and claudin-4 mRNA levels, as well as an over-expression at the protein level has
been reported in ovarian carcinomas, as compared to ovarian cystadenomas and
normal ovary (Rangel, Agarwal et al. 2003). It has been hypothesized that claudin-3,
when over expressed, might interfere with normal tight junction formation and
function (Rangel, Agarwal et al. 2003). An over-expression of claudin-3 might be
100
involved also in the impairment of tight junctions in endothelial cells, that leads to the
increased vascular permeability registered in hypoxia (Gonzalez and Wood 2001).
CD24, a sialoglycoprotein that is anchored to the cell surface by a GPI anchor
(Fischer, Majdic et al. 1990), is expressed on many tumor cells and is a ligand for P-
selectin (Aigner, Sthoeger et al. 1997), a Ca2+-dependent endogenous lectin that can
be expressed by activated vascular endothelium and platelets. P-selectin interacts with
P-selectin glycoprotein ligand-1 on leukocytes, taking part in the process of leukocyte
capture, rolling and extravasation (Yang, Furie et al. 1999). The differential
expression of surface antigens has been studied on activated endothelium and it has
been shown that CD24, among others, was increased in endothelial cells upon
stimulation with either TNF-α, interferon γ or thrombin (Favaloro 1993). Our results
show that CD24 mRNA expression is up regulated at late times of exposure to
hypoxia in HUVEC cells. Although the biological meaning of this observation
remains unclear, CD24 expression might be regarded as a useful marker of hypoxic
activation in vascular endothelial cells.
Tetranectin, a tetrameric protein isolated from human plasma, has a specific binding
affinity for sulfated polysaccharides and the kringle 4 domain of plasminogen (Wewer
and Albrechtsen 1992). It is induced during the mineralization phase of osteogenesis,
and a role of this protein in human disorder affecting bone and connective tissue has
been postulated (Wewer, Ibaraki et al. 1994). In situ hybridization studies for
tetranectin messengers on tissue sections of colon carcinomas and normal colon
tissues revealed a strong and distinct hybridization signal of stromal cells in colon
carcinomas but not of tumor cells. Only a few stromal cells were labeled in the normal
101
colon (Wewer and Albrechtsen 1992). Immunohistochemically, tetranectin was found
in a fibrillar-like pattern in the extracellular matrix around the tumor islands and was
not detectable in the normal colon stromal tissue. We have shown (see above) by both
transcriptomic and proteomic experiments (Bumke, Neri et al. 2003) that tetranectin is
strongly up regulated in vitro by serum starvation in normal human fibroblasts, but
that this up regulation is dampened by a simultaneous shift to an acidic milieu. The
finding that tetranectin gene expression is also strongly up-regulated in HUVEC
exposed to hypoxia reinforce the possibility that the plasmin cascade play a
remarkable role in the degradation and remodeling of the extracellular matrix, one of
the key steps in tumor angiogenesis.
When one examines the list of the genes which resulted to be strongly down regulated
by exposure to hypoxia of HUVEC cells, the most striking findings is by far the
decreased expression of messengers for bFGF, especially in view of the concomitant,
strong up-regulation of VEGF. Hypoxia has been shown to lead to an increase in
bFGF mRNA levels in human breast carcinoma cells (Le and Corry 1999), in
pulmonary vascular pericytes (Wang, Xiong et al. 2000) and, in vivo, in the adult
mouse retina (Grimm, Wenzel et al. 2002). By contrast, it has been reported that, in in
vivo xenografts of rat and human prostatic cancers, exposure to hypoxia leads to an
increase in VEGF, but not bFGF protein expression (Joseph and Isaacs 1997).
Among the genes of the extracellular matrix, a particularly interesting case is
represented by procollagen proline 4 hydroxylase A and procollagen lysine 4
hydroxylase 1 and 2.
102
As already discussed, exposure of HUVEC to hypoxia strongly up-regulates the
expression of procollagen proline 4-hydroxylase 1 gene (P4HA). In collagen
synthesis, P4HA is a key enzyme that catalyzes the formation of 4-hydroxyproline, an
essential residue for the folding of the procollagen polypeptide chains into triple
helical molecules (Kivirikko and Pihlajaniemi 1998). Many observations point to the
fact that systemic and cellular hypoxia modulates collagen synthesis in several types
of cells (Falanga, Martin et al. 1993; Durmowicz, Parks et al. 1994; Ostadal, Kolar et
al. 1995; Kim, Kang et al. 1996). Lysyl hydroxylase (PLOD) catalyzes the
hydroxylation of lysine in -X-Lys-Gly- sequences in collagens and exists in three
different isoforms (Kivirikko and Pihlajaniemi 1998). The resulting hydroxylysine
residues have two important functions: they act as attachment sites for carbohydrate
units and are essential for the stability of the intermolecular collagen cross-links
(Kivirikko and Pihlajaniemi 1998). The importance of proper lysyl hydroxylase
function is clearly seen in patients with Ehler-Danlos syndrome type VI, an autosomal
recessive disorder of connective tissue characterized by hyperextensible, friable skin
and joint hypermobility. Severe scoliosis and ocular fragility are present in some
patients. The disease is associated to a relative deficiency of specific hydroxylysine-
derived collagen crosslinks, owing to a reduced functionality of the enzyme (Ha,
Marshall et al. 1994; Pasquali, Still et al. 1997).
Our data show that both PLOD1 and PLOD2, but not PLOD3, are up regulated at late
times of exposure to hypoxia in HUVEC. We examined the promoter region of human
PLOD1 and PLOD3 and of the murine PLOD2 genes and found that both hPLOD1
and mPLOD2 contain a putative functional hypoxia responsive element (HRE). A
functional HRE consists of a pair of contiguous transcription factor binding sites, at
least one of which contains the core HIF-1 binding sequence 5’-RCGTG-3’
103
(Semenza, Jiang et al. 1996). In genes that contain two contiguous HIF-1 binding
sites, they are arranged as either directed or inverted repeats, separated by 4-12 bp.
Presence of a single binding site is necessary but not sufficient for HRE function
(Semenza, Jiang et al. 1996). Figure 15 shows a comparison of sequences of promoter
regions of hPLOD1 (GenBank AF490514, positions 453-489) and mPLOD2
(GenBank AF283255, positions 503-534) with those of other genes that have been
demonstrated to be under the transcriptional control of HIF-1. hPLOD1 promoter
hPLOD1 5’-GTGTCCGTGTCTCTGAACGCATCCGTGCCCACCCTCA-3’ hENO1 5’-GAGTGCGTGCGGGACTCGGAGTACGTGACGGAGCCCC-3’ hALDO 5’-CCCCCTCGGACGTGGACTCGGACCACAT-3’ hEPO 5’-GGGGCTGGGCCCTACGTGCTGTCTCACACAGCCTGTC-3’ mPLOD2 5’-AGCCCCGGCGTGAGGCGGTGCACGTAGGCGTA-3’ mGLUT1 5’-TCCACAGGCGTGCCGTCTGACACGA-3’ mLDHA 5’-GCCCAGCCTACACGTGGGTTCCCGCACGTCCGCTGGG-3’ Figure 15: Comparison of sequences of promoter regions of hPLOD1 and mPLOD2 with other HIF-1 regulated genes. The promoter regions of hPLOD1 (GenBank AF490514, positions 453-489) and mPLOD2 (GenBank AF283255, positions 503-534) were aligned with those of other human and murine genes whose expression has been demonstrated to be controlled by the transcription factor HIF-1. hPLOD1, human procollagen lysyl hydroxylase 1; hENO1, human enolase alpha (Semenza, Jiang et al. 1996); hALDO, human aldolase A (Semenza, Jiang et al. 1996); hEPO, human erythropoietin (Semenza, Nejfelt et al. 1991); mPLOD2, murine procollagen lysyl hydroxylase 2; mGLUT1, murine glucose transporter 1 (Ebert, Firth et al. 1995); mLDHA, murine lactate dehydrogenase A (Firth, Ebert et al. 1994).
104
sequence is very similar to that found in human enolase alpha gene (Semenza, Jiang et
al. 1996), mPLOD2 sequence strongly resembles to that of murine glucose transporter
1 (Ebert, Firth et al. 1995). Although these findings per se are not sufficient to draw
any conclusion about the functionality of the HREs identified, and await confirmation
based on transient transfection studies using constructs containing the PLOD
promoter(s) linked to a reporter gene, they at least suggest that the PLOD 1 and 2
gene regulation might be controlled by the HIF-1 transcription factor. A coordinated
up-regulation of proline and lysine hydroxylases involved in synthesis and structural
organization of collagens, the major components of the extracellular matrix, might be
crucial for the remodeling of the extracellular matrix that occurs in the early steps of
hypoxia-induced tumor angiogenesis.
In the ontological category of ligands, our analysis identified an interesting up-
regulated gene, Del-1 (developmentally regulated, endothelial locus 1). The protein
encoded in this locus contains three EGF-like repeats, homologous to those in Notch
and related proteins, including an EGF-like repeat that contains an RGD motif, and
two discoidin I-like domains (Hidai, Zupancic et al. 1998). The expression pattern of
Del-1 is unique in that it is initially expressed already in endothelial progenitor cells
of the extraembryonic mesoderm, but then declines and disappears completely by
birth (Hidai, Zupancic et al. 1998). It is completely absent in practically all adult
tissues (except in the superficial layer of articular cartilage (Pfister, Aydelotte et al.
2001)), but its expression is strongly reactivated in some tumor cell lines (Hidai,
Zupancic et al. 1998). Del-1 is deposited in the extracellular matrix, where it promotes
endothelial cell attachment and migration by binding, via its RGD motif, to the αvβ3
integrin receptor (Penta, Varner et al. 1999). It has been shown also that Del-1 is
105
massively expressed in the modified extracellular matrix of solid tumors in breast
carcinoma, melanoma and colon carcinoma specimens, being associated with both
tumor cells and angiogenic endothelial cells (Aoka, Johnson et al. 2002). Del-1
promises to become an interesting marker for tumor angiogenesis, and a new target
for therapeutic intervention.
3.2.4.2. Proteomics
A proteomic study was performed to complement the transcriptomic analysis since
proteins carry dynamic (e.g. phosphorylation) and static modifications (e.g. disulfide
linkage) that may not be apparent from genomic information or from mRNA
abundance (Corthals, Wasinger et al. 2000). Furthermore, a study comparing 2D-gel
protein-expression measurements with the corresponding message data derived from
differential gene expression showed that the correlation between mRNA and the
cognate protein is poor, suggesting that post-transcriptional regulation of gene
expression is a frequent phenomenon (Anderson and Seilhamer 1997).
We measured more than 700 trypsin-digested samples derived from 2D gels by
tandem mass spectrometry. About a third of the measured spots were clearly
identified with two or more peptides and a good correlation coefficient (> 2.5) by the
SEQUEST algorithm, one third of the samples could only be assigned with one
peptide.
The expression of procollagen lysine, 2-oxoglutarate 5-dioxygenase 2, (procollagen
lysyl hydroxylase 2, PLOD2) was found to be up regulated in HUVECs incubated for
48 hours in hypoxic conditions (Fig. 14, spots 1 to 3). This result mirrors the
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corresponding increase in PLOD2 mRNA, observed in the transcriptomic experiment
and already discussed (see above).
Another remarkable difference is represented by the increased expression of the α-
enolase protein in hypoxic HUVEC (Fig. 14, spots 4 and 5), with respect to normoxic
controls. The glycolytic enzyme α-enolase gene is known to be controlled by the
transcriptional complex HIF-1 (Semenza, Jiang et al. 1996), yet in our cell model
system it did not show an hypoxia-dependent up-regulation at the level of mRNA
transcripts (see Table 1). The finding of an increased expression of the enolase α at
the protein level might indicate some additional post-translational regulation of the
expression of this enzyme in hypoxic HUVEC.
The poor correspondence between transcriptomic and proteomic analysis is not an
unusual finding. In the above described study, aimed at investigating the modulation
of gene expression by extracellular pH variations in human fibroblasts and making
use of the same transcriptomic and proteomic approach (Bumke, Neri et al.), only one
out of six proteins differentially expressed in the proteomic analysis was also found to
be modulated at the transcriptional level. Reasons for this are inherent to the very
different technical limitations imposed by sample preparation, dynamic range and
sensitivity of the two techniques that ought to be regarded as complementary rather
than alternative.
The most striking observation of our proteomic analysis was the extremely small
number of differentially expressed proteins detected in hypoxic vs. normoxic
HUVEC. In particular, membrane protein solubility problems limited the number of
detectable proteins to the most abundant ones, with no significant variations between
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the two conditions under study. We have established a cell surface protein
biotinylation procedure that ought to facilitate the recovery of biotin-labeled
membrane proteins by avidin affinity chromatography, followed by their proteolytic
digestion and quantitative LC-MALDI-TOF of the obtained peptide mixture
(Scheurer, Rybak et al. 2005). Tandem mass spectrometry of the relevant peptide
fractions would ultimately lead to the identification of the interesting, modulated
peptides.
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3.3. Identification and relative quantification of membrane proteins by surface
biotinylation and two-dimensional peptide mapping
Membrane proteins play a variety of fundamental roles in all living organisms. In
particular, transmembrane proteins and proteins anchored to the cell membrane
represent more than 30% of all proteins in the human genome (Paulsen, Sliwinski et
al. 1998; Wallin and von Heijne 1998), and more than two-thirds of the known protein
targets for drugs (Stevens and Arkin 2000). However, in spite of their relevance,
membrane proteins have often escaped a systematic analysis and quantification in
biological systems, due to their limited solubility in water and their relatively low
abundance (Scheurer, Rybak et al. 2004). Methods for the identification and relative
quantification of membrane proteins are likely to have an impact in many areas of
biological research, including immunology and pharmaceutical sciences.
When monoclonal antibodies specific to membrane proteins are available, the relative
quantification of membrane proteins in tissues and cells is facilitated by
immunohistochemical and fluorescence-activated cell sorting analysis (FACS)
(Herzenberg, Parks et al. 2002). However, antibody-based methods have some
limitations in the simultaneous detection and quantification of a large number of
different membrane proteins.
Since its introduction in 1975, 2D-PAGE has met an enormous success for the
simultaneous separation and relative quantification of several hundreds of proteins in
biological specimens. Even though successful examples (Celis, Ostergaard et al.
1996; Celis, Rasmussen et al. 1996; Celis, Celis et al. 1999; Celis, Celis et al. 2002)
109
and technical improvements have become available, 2D-PAGE methodologies are
often unsuitable for the analysis of membrane proteins, primarily because of poor
protein solubility in the buffers used for protein separation in the first dimension
(Santoni, Molloy et al. 2000).
Isotope-coded affinity tag (ICAT) methodologies (Gygi, Rist et al. 1999) are rapidly
establishing themselves as a valuable tool for the relative quantification of proteins in
biological samples, but the need to label primary amino groups in membrane proteins
(rather than thiol groups) may result in complex distributions of fractionally labeled
peptides, thus complicating peptide recovery, separation and relative quantification
(Haynes and Yates 2000; Patton, Schulenberg et al. 2002). Other mass-spectrometry
based approaches (such as the multi-dimensional protein identification technique
(Washburn, Wolters et al. 2001) or the shotgun analysis of soluble and membrane
proteins according to Wu et al. (Wu, MacCoss et al. 2003) have shown remarkable
success in the simultaneous identification of hundreds of membrane proteins, but are
inherently not quantitative.
Surface biotinylation with reactive chemical derivatives of biotin, followed by
purification on streptavidin, has been investigated as a method to restrict proteomic
analysis to membrane proteins. These approaches have clearly shown a potential for
the selective chemical modification and recovery of membrane proteins and
extracellular proteins, both in vitro (Busch, Hoder et al. 1989; Brandli, Parton et al.
1990; Sabarth, Lamer et al. 2002) and ex vivo (Rybak, Scheurer et al. 2004).
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In this thesis, we describe a method for the simultaneous recovery, separation,
identification and relative quantification of membrane proteins. After covalent
modification with a cleavable reactive ester derivative of biotin, cells are lysed in the
presence of detergents and membrane proteins are purified on streptavidin-coated
resin. Proteins are then eluted and tryptically digested. The resulting peptides are used
to generate a two-dimensional map, based on HPLC separation in the first dimension
and mass-spectrometric analysis in the second dimension. The use of internal peptide
standards in MALDI-TOF mass spectrometry facilitates the relative quantification of
peptides (and thus of the corresponding protein) (Nelson, McLean et al. 1994). We
have exemplified this method by studying i) the detection of peptides from a
biotinylated BSA-spike, added to an aliquot of HEK biotinylated membrane proteins
before capture on streptavidin Sepharose, with respect to the non-spiked counterpart,
and ii) the relative changes of membrane protein expression of HUVEC cells, cultured
in normoxic or hypoxic conditions. The results exposed in the previous section report
about the comparative transcriptomic and proteomic analysis of the response of
HUVEC cells to hypoxia, using Affymetrix gene chip technology and 2D-PAGE
(Scheurer, Rybak et al. 2004).
3.3.1. Surface biotinylation and two-dimensional peptide mapping for the proteomic
study of membrane proteins
We have established a method (termed “2D peptide mapping”) for the selective
isolation and relative quantization of membrane proteins (Figure 16). For sample
preparation, the cell surface is biotinylated with the biotin derivative sulfo-NHS-SS-
biotin, a cleavable and water-soluble biotinylation reagent specific for primary amino
111
groups. The biotinylated cells are lysed in the presence of 0.2% SDS and 2% NP-40
and biotin-modified proteins are then purified on a streptavidin-coated resin, eluted by
reduction of the biotin linker and digested with trypsin. The resulting tryptic peptides
are fractionated by microcapillary reversed-phase HPLC, followed by MALDI-TOF
MS analysis of the HPLC fractions. This procedure yields a collection of spectra,
which can be displayed as a two-dimensional peptide map, with HPLC fractions in the
first dimension, mass/charge ratios (m/z) in the second dimension, and MS peak
intensity represented with a grayscale. For this purpose, we developed a flexible
igure 16
F . Schematic representation of the experimental methodology for the identification and relative uantification of membrane proteins by surface biotinylation and two-dimensional peptide mapping. q
Cell surface proteins are covalently modified with sulfo-NHS-SS-biotin, a biotin derivative carrying a cleavable linker and reactive for primary amino groups. The cells are lysed in the presence of detergents and biotin-labeled proteins are purified on a resin coated with streptavidin. Isolated proteins are enzymatically digested following their elution from the resin. The resulting peptides are fractionated by reversed-phase microcapillary high-performance liquid chromatography (RP-HPLC). Eluting fractions are splitted in two: one set of fractions is analyzed by matrix-assisted laser ionization time of flight mass spectrometry (MALDI-TOF MS) and the other set by microcapillary-liquid chromatography tandem mass spectrometry (µLC-MS/MS). A two-dimensional peptide map (2D-peptide map) is established with the data obtained by MALDI-TOF MS, reporting the HPLC fractions on the y-axis and the mass to charge ratio of the measured peptides on the x-axis.
112
software (termed “Spectational”; the software is available f on the following website:
ich is not used for
hen the 2D peptide mapping procedure is performed in parallel on two closely
the peptide LVNEVTEFAK
http://www.proteios.org/proj/spectational/Spectational.zip; Password: quantprot),
which allows the two-dimensional display of sequential mass spectra as a grayscale
map, as well as normalization, zooming and scaling procedures.
After HPLC, a portion of each chromatography fraction (wh
MALDI-TOF analysis) may be submitted to microcapillary-liquid chromatography
tandem mass spectrometry, thus facilitating protein identification (McCormack,
Schieltz et al. 1997).
W
related samples (e.g., the same cell lines cultured in different experimental
conditions), the relative intensity of MALDI-TOF peaks (i.e., bars in the 2D map) can
be used for relative protein quantification. The MALDI ionization technique features
high sensitivity (low-femtomole levels of peptides can routinely be detected), high
speed of analysis, and is easy to perform and tolerant to modest amounts of salts and
detergents. While the absolute intensity of the ion signal of a peptide in different
measurements may depend on experimental parameters, variations can be minimized
by including internal standards in the peptide samples.
We measured five replicates of a dilution series of
ranging from 0.1 pmol to 40 pmol by MALDI-TOF MS, in the presence or in the
absence of internal standards. Figures 17 A and 17 B show that standard deviations
could substantially be reduced by the use of internal standards (Nelson, McLean et al.
1994), allowing the detection of at least two-fold differences in peptide abundance.
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In order to assess ion suppression effects (Kratzer, Eckerskorn et al. 1998; Wall,
Berger et al. 2002) in peptide mixtures, we studied the influence of an increasing
amount (0.1, 1, 2, 4, 6, 8, 10, 20 or 40 pmol) of competitor peptide
KVPQVSTPTLVEVSR on the ion signal intensity of 5 pmol peptide
LVNEVTEFAK. The competitor was mixed with 5 pmol of peptide LVNEVTEFAK,
and five replicates of each sample were measured by MALDI-TOF MS. The addition
Figure 17. MALDI-TOF MS with test peptides. Five replicates of a dilution series (ranging from 0.1 pmol to 40 pmol) of a test peptide with the sequence LVNEVTEFAK were measured by MALDI-TOF mass spectrometry. (A) The averaged peak signal intensities are plotted relative to the corresponding peptide amounts. (B) The same dilution series (five replicates) were mixed with 5 pmol of a standard peptide with the sequence KVPQVSTPTLVEVSR. The averaged peak signal intensities of the test peptide were normalized to the peak signal intensity of the standard peptide and plotted versus the corresponding amounts of test peptide. (C) Five pmol of the peptide LVNEVTEFAK were analyzed with increasing amounts of the peptide KVPQVSTPTLVEVSR (0.1 pmol to 40 pmol). Panel C displays the averaged peak signal intensities of 5 pmol of the peptide LVNEVTEFAK relative to different amounts of the peptide KVPQVSTPTLVEVSR added. (D) Panel D shows a plot of the ratio between the ion signal intensities of peptide LVNEVTEFAK and peptide KVPQVSTPTLVEVSR plotted versus the ratio of their relative concentration (logarithmic scale).
114
of increasing amounts of competitor peptide led to a decrease in ion signal intensity
for peptide LVNEVTEFAK (Figure 17 C). This observation suggests that relative ion
signal intensity can be used for the relative quantification of peptides in samples
containing comparable total amounts of peptides, while suppression effects may be
experienced in peptide mixtures that greatly differ in total peptide quantity.
However, the ratio of ion signal intensities of the peptide LVNEVTEFAK and the
competitor peptide was found to be proportional to the ratio of their relative amount
(Figure 17 D), confirming that the use of an internal standard peptide helps
minimizing the influence of ion suppression on the relative quantification analysis.
3.3.2. Detection of tryptic peptides derived from a BSA-spike added to an HEK
membrane protein extract
We performed a spiking experiment with bovine serum albumin, in order to determine
whether our method is suitable to the detection of differences in protein composition
of biotinylated samples.
We carried out a biotinylation experiment on two independent samples of HEK cells.
Biotinylated proteins were extracted from each sample and, to one of them, 100
pmoles of biotinylated BSA were added. The two samples were processed in parallel
and the tryptic peptides obtained were subjected to two-dimensional peptide mapping,
as described in the Materials and Methods section.
At least three different signals, corresponding to BSA peptides, could be detected only
in the BSA-spiked sample. As an example, Figure 18 A shows a portion of the 2D
peptide map obtained for fractions 18 to 28 of the BSA-spiked sample versus the non-
spiked control, after linear software normalization of peptide signal intensities relative
to the MS intensity of the internal standard peptide. In the boxed region of the map,
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Figure 18. 2D peptide map of HEK cell surface proteins, in the presence or absence of a biotinylated BSA spike. Replicate HEK cultures were biotinylated and processed in parallel. Biotinylated BSA was added as a spike to one-half of the replicates before capture the samples on streptavidin Sepharose, processing them for HPLC separation and MALDI-TOF analysis and establishing a 2D peptide map. Panel A shows a portion of the 2D peptide map corresponding to HPLC fractions 18 to 28 (y-axis) and to m/z interval 700-2500 (x-axis). Peak signal intensities were normalized to the intensity of an internal standard peptide added at known amount to each HPLC fraction prior to MALDI-TOF MS measurements. Asterisks indicate BSA-spiked fractions. Panels B and C display the MALDI-TOF spectra of the non-spiked and spiked fraction 26, respectively corresponding to the section marked with a square in Panel A. The white arrows in Panel A and C point to a tryptic peptide of mass 1439.9, specifically present only in the BSA-spiked sample, and corresponding to the BSA peptide RHPEYAVSVLLR, whose calculated monoisotopic mass is 1439.8117.
the presence of a signal in the fraction 26 of the BSA-spiked sample, which is absent
in the control, is clearly detectable. Panels B and C show the MALDI-TOF spectra
corresponding to the boxed region of the non-spiked (panel B) and of the BSA-spiked
(panel C) samples. A peak at m/z ratio of 1439.9 is clearly detected and corresponds
116
to the BSA peptide RHPEYAVSVLLR, whose calculated monoisotopic mass is
1439.8117.
3.3.3. Two-dimensional peptide mapping of membrane proteins of HUVEC cell
cultures
We explored the applicability of the 2D peptide mapping technique for the study of
differentially expressed membrane proteins in HUVEC exposed to hypoxic (2% O2)
or normoxic (21% O2) growth conditions.
Figure 19 shows a section of the 2D peptide map from replicate HUVEC cultures,
Figure 19. 2D peptide map of cell surface proteins of HUVEC grown in hypoxic conditions. Replicate HUVEC cultures were grown in hypoxic conditions and processed in parallel as described in Materials and Methods. A 2D peptide map was established using the Spectational software. The figure displays a region of the 2D peptide map, corresponding to HPLC fractions 20 to 40 (y-axis) and m/z interval 500-3500 (x-axis). Peak signal intensities were normalized to the intensity of an internal standard peptide added at known amount to each HPLC fraction prior to MALDI-TOF MS measurements. Each fraction was also analyzed by µLC-MS/MS to identify the proteins contained in the sample. Peptides representing identified proteins are indicated with a number, which corresponds to the protein identification in Supplementary Table 1 in Appendix A (see also the text for reference to our website).
117
grown for 48 hours in hypoxic conditions. The replicate samples were processed in
parallel and independently from each other according to the scheme depicted in
Figure 16. MALDI-TOF MS spectra of HPLC fractions 20-40 are displayed in
sequential order, after linear software normalization of peptide signal intensities
relative to the MS intensity of the internal standard peptide.
The commercial preparation of the internal standard peptide we used appears as a
mixture of three different peptides (observed, in the average, at m/z 1639.4, 1538.4
and 1511.3). Signals of the internal standard peptides and of most tryptic peptides can
be observed for all fractions at comparable positions and relative intensity, thus
confirming the reproducibility of the 2D-peptide mapping procedure in independent
experiments performed in duplicate.
In total, 71 proteins were identified, 41 % of which could be assigned as type I
membrane proteins (PECAM-1, integrins, vascular endothelial cadherin and others),
integral membrane proteins (monocarboxylate transporter and sodium channel protein
type I alpha subunit) and membrane associated proteins (Caveolin-1, alpha-1 catenin),
by tandem mass spectrometry. Supplementary Table 1 (Appendix A; also available at
the website: http://www.pharma.ethz.ch/bmm/div/2D_pepmap_HUVEC) shows a list
of the proteins identified in the hypoxic HUVEC samples and shown in Figure 19,
with their SwissProt accession number, as well as the corresponding peptides and
fraction number. In the course of this experiment, we also identified extracellular
matrix proteins (13%) and cytoplasmic proteins (32%). Another part of the proteins
identified consists of contaminants (keratins), serum components (such as serum
albumin) and hypothetical proteins.
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3.3.4. Comparison of membrane protein expression in HUVEC cells exposed to
hypoxia and normoxia
While the majority of peptides from biotinylated surface proteins did not show
substantial changes of expression in the normoxic or hypoxic conditions, some
peptides were reproducibly found to be more abundant in one of the two experimental
conditions. Figure 20 A, C and E show portions of the 2D peptide map obtained,
normalized to the signal intensity of the internal standard peptide peak. Arrows point
to peptide signals which were either found not to be modulated between the two
conditions under study (Fig. 20 C) or which were specifically present only in one
condition (Fig. 20 A and 20 E). Figure 20 B, D and F represents the MALDI-TOF MS
spectra corresponding to the 2D peptide map sections shown in Fig. 20 A, C and E,
respectively (only one of the two replicates shown) and confirm the specificity of
peptide peak detection in normoxic or hypoxic HUVEC samples.
The peptide indicated by “a” in the hypoxic replicate samples (Fig. 20 A, B) was
identified by LC-MS/MS as NEGVATYAAAVLFR, a peptide of beta-catenin, a
protein involved in the Wnt growth factor signaling cascade (Lampugnani and Dejana
1997) and component of adherens junctions (Biswas, Canosa et al. 2003), suggesting
that this protein could be over-expressed in hypoxic conditions. The peptide “b” (Fig.
20 C, D) was identified as the peptide KPLIGTVLAMDPDAAR of VE-cadherin. The
amount of this protein and of alpha-catenin (not shown), two other major components
of adherens junctions, appear not to be modulated by hypoxia in 2D peptide mapping
analysis. These findings were confirmed by western blotting analysis, performed
using normoxic and hypoxic HUVEC whole cell lysates (Fig. 20 H), showing that in
hypoxic HUVEC samples beta-catenin is significantly over-expressed with respect to
the normoxic counterpart, while VE-cadherin and alpha-catenin are not modulated.
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Figure 20. Comparative analysis of HUVEC grown in hypoxic and normoxic conditions. Panels A, C and E show sections from 2D-peptide map of two replicates (“1” and “2”) of normoxic (“N”) and hypoxic (“H”) HUVEC samples, respectively. Arrows indicate reproducible differences. The MALDI-TOF MS spectra corresponding to the 2D peptide map sections, presented in panels A, C and E are shown in panels B, D and F, respectively. The white arrows (“a” to “d”) point to relevant differences detected in the two samples. The peptide indicated by the arrow “a” (panels A and B) and over-expressed in hypoxic HUVEC was identified by LC-MS/MS as NEGVATYAAAVLFR, a peptide of beta-catenin. The peptide “b” (panels C and D) was identified as the peptide KPLIGTVLAMDPDAAR of VE-cadherin. Peptides “c” and “d” (panels E and F) were identified as GGVNDNFQGVLQNVR and VDVIPVNLPGEHGQR, belonging respectively to thrombospondin 1 and fibronectin. The over-expression of beta-catenin, thrombospondin 1 and fibronectin in hypoxic HUVEC, as well as the lack of modulation of VE-cadherin and alpha-catenin in the two conditions were confirmed by western blot analysis. Panel H shows immunoblot analysis of HUVEC whole cell lysates. Two replicate cultures (“1” and “2”) of hypoxic (“H”) and normoxic (“N”) HUVEC were lysed and 10 µg of protein were separated by SDS-PAGE. The proteins were transferred to a nitrocellulose membrane and probed for the different proteins. Panel G shows a SDS-PAGE gel replicate, stained with Sypro Ruby, as a loading control.
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Some more peptides were also found to be up regulated in hypoxic HUVEC. For
example, peptide “c” and peptide “d” (Fig. 20 E, F) were identified as GGVN
DNFQGVLQNVR and VDVIPVNLPGEHGQR, belonging respectively to
thrombospondin 1 and fibronectin. Also in this case, Western blot analysis of
normoxic and hypoxic HUVEC whole cell lysates (Fig. 20 H) could confirm the over
expression of these two proteins in hypoxia.
A Sypro Ruby stained replicate of the SDS-PAGE gel is shown in Fig. 20 G as a
loading control, indicating comparable amounts of proteins in the four different lanes.
3.3.5. Discussion
The 2D peptide mapping procedure described in this thesis allows a relative
quantification of surface proteins in parallel cell cultures exposed to different
experimental conditions.
In a simple experimental setup, represented by biotinylated membrane proteins from
HEK cells, to which a BSA-spike was added, we could identify different BSA
peptides specifically present only in the spiked sample. In HUVEC cells, grown in
normoxic or hypoxic conditions, we could detect peptides that are differentially
expressed in one of the two conditions. Until now, abundant surface proteins of
endothelial cells (including integrins, collagens, endoglin and a number of CD
antigens) were preferentially identified. However, improved protein solubilization
after elution from streptavidin resin or direct tryptic digestion on the resin may further
increase the number of proteins identified.
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The major part of the proteins identified in our analysis was membrane proteins or
extracellular matrix components. However, we also observed peptides corresponding
to cytoplasmic proteins. It has been shown that biotinylation of intracellular proteins
is greatly reduced when using sulfo-NHS-SS-Biotin as compared to sulfo-NHS-LC-
Biotin (Peirce, Wait et al. 2004), suggesting that the reducing milieu of the cytoplasm
could be responsible for the cleavage of the disulfide bond between protein and biotin
and as a consequence prevent the isolation of cytoplasmic proteins. It is possible,
nonetheless, that cytoplasmic proteins are co-purified by virtue of a strong interaction
with membrane proteins and despite of the strong detergents used, or are not
completely eliminated during the washing of the streptavidin-coated resin.
MALDI-TOF MS ion signal intensity allows the relative quantification of proteins in
2D peptide mapping. Ion suppression effects are minimized both by the use of internal
standards and by the comparison of closely related biological specimens (e.g., the
same cell cultures grown in different experimental conditions). The analysis of
replicate HUVEC cultures depicted in Figures 19 and 20 illustrates the reproducibility
of the procedure, while outlining surface proteins that are differentially expressed in
normoxic and hypoxic conditions. Like other methodologies [e.g., ICAT (Gygi, Rist
et al. 1999)], 2D peptide mapping only provides a relative quantization of proteins in
closely related samples. Absolute protein quantification is hindered not only by
limitations of mass spectrometry, but also by differences in tryptic digestion
efficiency among individual proteins.
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The identification of beta-catenin as a protein over expressed in hypoxic conditions
deserves some considerations. Beta-catenin is a component of adherens junctions,
which mediates cell-cell adhesion by providing a physical link between the
transmembrane protein vascular endothelial-cadherin and the actin cytoskeleton via
interaction with alpha-catenin (Lampugnani and Dejana 1997). Those four proteins
have been identified in our analysis (Figure 4 and 5), revealing a tight interaction of
VE-cadherin with its intracellular binding partners (Huber and Weis 2001; Stow
2004), which resists washing steps in 1% NP-40 and 0.1% SDS. However, the levels
of expression of VE-cadherin and of alpha-catenin appeared not to be influenced by
HUVEC exposure to hypoxic growth conditions. In hypoxic conditions, beta-catenin
expression does not appear to be differentially regulated at the mRNA level
(Scheurer, Rybak et al. 2004), thus suggesting a post-transcriptional nature of beta-
catenin over-expression in hypoxia. A functional link between beta-catenin
accumulation and endothelial cell proliferation has recently been suggested (Biswas,
Canosa et al. 2003).
Thrombospondin 1 (TSP-1) is a glycoprotein with major roles in cellular adhesion and
vascular smooth muscle cell proliferation and migration and is also a well known
inhibitor of angiogenesis (Tucker 2004). It has been already shown in the past that
exposure of endothelial cells to hypoxic environment up regulates the TSP-1 gene and
protein expression (Phelan, Forman et al. 1998). Although less clear and concordant,
other data of the literature have shown both in in vitro (Chen, Yang et al. 2004) and in
in vivo (Berg, Breen et al. 1998) studies that levels of fibronectin protein and/or
transcript can also be up regulated by exposure to hypoxia.
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Recent methodologies based on the multidimensional chromatographic separation of
membrane fractions at extreme pH values (Wu, MacCoss et al. 2003) or protein
biotinylation (Peirce, Wait et al. 2004; Zhao, Zhang et al. 2004) have allowed the
identification of a large number of membrane proteins. In contrast to 2D peptide
mapping, these methods, however, are not inherently quantitative. While our
technology is applicable to several biological systems, the most immediate challenges
are likely to include the comparison of closely related cell lines (Speers and Cravatt
2004) (e.g., metastastic vs. non-metastatic cancer cell lines (Clark, Golub et al.
2000)), or cells of the immune system at different stages of activation (e.g., dendritic
cells or natural killer cells).
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3.4. In vivo protein biotinylation for the identification of organ-specific antigens
accessible from the vasculature.
The endothelium is a highly dynamic structure, morphologically and functionally
adapted to meet the unique needs of the underlying tissue (Madri and Williams 1983;
Auerbach, Alby et al. 1985; Aird, Edelberg et al. 1997). Recently, a comprehensive
description of endothelial cell (EC) diversity as a result of differences in vascular
beds, differentiation programs, and distinct adaptation to physiological and
pathological changes has been proposed (Chi, Chang et al. 2003). Indeed, different
patterns of global gene expression are emerging for ECs of arterial, venous and
lymphatic origin (Lawson and Weinstein 2002; Chi, Chang et al. 2003; Hirakawa,
Hong et al. 2003; Veikkola, Lohela et al. 2003; Yamashita 2004) . In addition, vessel
size (Muller, Hermanns et al. 2002), flow properties (Braddock, Schwachtgen et al.
1998), anatomical location (Pasqualini and Ruoslahti 1996; Chi, Chang et al. 2003),
physiological and developmental processes (Cleaver and Melton 2003) further
modulate EC gene expression. Other lines of evidence (Wyder, Vitaliti et al. 2000;
Oh, Li et al. 2004) indicate that different tumors can have a differential influence on
the expression of EC surface proteins. Furthermore, the ubiquitous distribution of
capillaries in all tissues and the partial accessibility of parenchymal cells via stromal
capillaries define body compartments, which are accessible to agents coming from the
bloodstream.
The high specialization of endothelia from different anatomical locations and
pathological conditions makes it possible to carry out a selective molecular targeting
of vascular structures, with immediate implications for imaging and therapy
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(Pasqualini and Ruoslahti 1996; Neri, Carnemolla et al. 1997; Birchler, Viti et al.
1999; Nilsson, Kosmehl et al. 2001; Posey, Khazaeli et al. 2001; Borsi, Balza et al.
2002; Carnemolla, Borsi et al. 2002; Castellani, Borsi et al. 2002; Halin, Rondini et al.
2002; Borsi, Balza et al. 2003; Halin, Gafner et al. 2003; Nanus, Milowsky et al.
2003; Santimaria, Moscatelli et al. 2003). Advances in this field will require an in-
depth proteomic analysis of ECs from different anatomical locations or pathological
conditions, followed by an extensive validation of the putative markers identified and
the development of specific binding molecules (e.g., human antibodies (Winter,
Griffiths et al. 1994)).
Jan Schnitzer and coworkers have pioneered the use of colloidal silica for the in vivo
coating of vascular structures in tumors and in normal organs (Jacobson, Schnitzer et
al. 1992; Durr, Yu et al. 2004). This physical modification allowed the isolation (by
centrifugation and fractionation) of silica-coated structures (luminal cell plasma
membranes and caveolae of the endothelium), providing enough material for
proteomic investigations, for example by immunization or by 2D-PAGE. Two
candidate targets, aminopeptidase P and annexin A1 were identified in lung and solid
tumors, respectively, and were validated by immunohistochemistry and scintigraphic
imaging (Oh, Li et al. 2004).
In principle, it would be desirable to characterize antigens accessible via the
vasculature using chemical methods, which allow the covalent modification, recovery
and identification of accessible proteins. Such methods would offer the possibility to
perform unbiased chemical reactions with all proteins in a certain compartment. The
chemical properties of the reagent (charge, lipophilicity, size, etc.) could be shaped to
126
control its distribution (in vivo or ex vivo), thus influencing the class of proteins to be
preferentially labeled for proteomic investigations.
In this paper, we describe the terminal perfusion of tumor-bearing mice with
sulfosuccinimidyl-6-(biotinamide)-hexanoate (sulfo-NHS-LC-biotin) for the in vivo
covalent modification of accessible proteins in normal organs and in solid tumors.
The resulting biotinylated proteins can then be recovered from the excised organ and
tumor tissue by purification on streptavidin resins, followed by on-resin proteolytic
digestion and mass spectrometric analysis. Our methodology led to reliable,
reproducible and efficient in vivo labeling of structures accessible from the
bloodstream. A number of biotinylated, organ-specific proteins could be identified in
the mouse kidney, liver, muscle and heart, as well as in two experimental tumor
models.
3.4.1. Terminal perfusion and in vivo biotinylation
Figure 21 illustrates the relevant steps of the in vivo biotinylation method, for the
discovery of markers preferentially expressed in different organs or in sites of disease
(such as solid tumors). The approach is based on the terminal perfusion of rodents
with a broadly reactive derivative of biotin. We used a charged active ester derivative
of biotin (sulfo-NHS-LC-biotin; Pierce), with an impaired diffusion through
biological membranes (Dentler 1995), but other reactive biotin derivatives could be
considered (see below). As a result, accessible proteins and certain amine-containing
glycolipids and phospholipids can be covalently modified with biotin. Biotinylated
proteins can be efficiently purified on streptavidin resins and submitted to a
comparative proteomic analysis, thus revealing markers which are differentially
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Target tissues (healthy organs, tumor)
Blood
Terminal perfusion of tumor-bearing mice
Extraction (SDS) and purification of biotinylated proteins on SA-Sepharose
Comparative proteomic analysis Target
identification
Biotinylation of accessible proteins
O S NH HN
O S NH HN
O
S NH
HN
O S
NH
HN
O
S
NH
HN
O
S
NH
HN
O
S
NH
HN
O
S
NHHN
O
S
NHHN
O
S
NHHN
Figure 21. Schematic representation of the relevant steps of the in vivo biotinylation method. Rodents are anaesthetized and subjected to the terminal perfusion with a broadly reactive derivative of biotin. As a result, accessible proteins and certain amine-containing glycolipids and phospholipids can be covalently modified with biotin. The excess of unreacted biotin derivative is quenched with Tris. Organs of interest and tumors are excised, homogenized and a total protein extract is prepared. Biotinylated proteins can be efficiently purified on streptavidin resins and submitted to a comparative proteomic analysis, thus revealing markers that are differentially expressed in organs and diseased tissues.
expressed in organs and diseased tissues. Healthy organs and the tumor tissue from
two different models [subcutaneous F9 teratocarcinoma in SvEv129 mice (Neri,
Carnemolla et al. 1997) and the RENCA carcinoma grafted orthotopically in a kidney
of BALB/c mice (Ahn, Jung et al. 2001)] were investigated.
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The large circulation of mice was perfused at a constant pressure of 100 mm Hg
through the left heart ventricle by means of a butterfly cannula fitted with a barb.
Mice were perfused first with PBS to wash away blood components, and then with a 1
mg/ml sulfo-NHS-LC-biotin solution, followed by a quenching step with PBS
containing a 50 mM solution of the primary amine Tris (hydroxymethyl)
aminomethane (Tris). All perfusion solutions contained Dextran 40 as a plasma
expander, and were pre-warmed at 38°C. Omitting the PBS wash step prior to in vivo
biotinylation improved the perfusion of the microvasculature of subcutaneously
induced tumors. Organs of interest and tumors were excised and subjected to further
investigation.
3.4.2. Histochemical analysis of biotinylated structures in organ and tumor sections
Histochemical analysis of organ and tumor sections with streptavidin-alkaline
phosphatase confirmed that discrete structures were labeled as a result of the in vivo
biotinylation reaction in normal organs and in tumors (Fig. 22). Normal organs
resulted to be easily accessible to the biotinylation reagent. Skeletal muscle, tongue,
kidney, liver (Fig. 22 e-h) and heart (not shown) of every biotinylated mouse showed
positive staining, while in negative control mice perfused with saline (Fig. 22 a-d) the
staining reaction was essentially negative. As expected, kidneys showed the strongest
staining, with a preferential accumulation in the tubular structures of the cortex and in
the glomeruli (Fig. 22 g). The liver was mainly stained around vascular structures
(Fig. 22 h). The biotinylation reagent reached also the liver parenchyma, although to
a variable extent in different portions of the liver as well as in different mice. In
muscle, strong staining was found around vessel structures as well as in the
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Figure 22. Histochemical and immunofluorescence analysis of organ and tumor sections. Organs from saline-perfused (a-d; i) or sulfo-NHS-LC-biotin perfused (e-h; j-l) F9 tumor-bearing SvEv129 mice were snap-frozen in cryoembedding medium. Sections from skeletal muscle (a,e), tongue (b, f) kidney (c, g), liver (d, h) or F9 tumor (i-j) were cut and subjected to histochemical staining with streptavidin:biotinylated alkaline phosphatase complex, followed by incubation with a freshly prepared solution of Fast-Red TR (a-j). Other section from F9 tumor were incubated with rat anti-mouse CD31 (1:100), followed by simultaneous incubation with goat anti-rat IgG-Cy3 conjugate (l) and streptavidin-Alexa Fluor 488 (k) to investigate the distribution of biotinylated structures relative to the localization of tumor endothelial cells. Bars = 100 µm intercellular spaces between the muscle fibers, where the endomysial capillaries are
located (Fig. 22 e). By contrast, the inner parts of the muscle fibers were not stained.
The same was true for the skeletal muscle fibers in the tongue where, in addition, the
lamina propria (sub-epithelial connective tissue containing numerous glands) was
intensely stained (Fig. 22 f).
Tumor tissues could be successfully biotinylated, with a preferential staining of
vascular structures; a section of a subcutaneous F9 tumor in a biotinylation reagent-
perfused SvEv mouse is shown in Fig. 22 j. However, the perfusion of tumors was
much less efficient and more heterogeneous compared to normal organs. While some
tumors showed a homogenous staining of virtually all vessels, more often the staining
was heterogeneous throughout the solid mass. An immunofluorescence co-staining of
tumor sections for biotin (Fig. 22 k) and the endothelial marker CD31 (Fig. 22 l)
130
revealed the co-existence of neighboring vascular structures, which were labeled to a
different extent. With the original 3-step perfusion protocol, only <10% of tumors
could be biotinylated successfully (42 perfused mice), whereas about 30% of tumors
showed successful staining when the PBS wash step was omitted (28 perfused mice;
data not shown). This modification had no effect on the perfusion efficiency of
normal organs.
3.4.3. Purification and identification of biotinylated proteins by proteomic techniques
Total proteins were extracted from the different excised organs or tumors and
quantified as described in Materials and Methods. Biotinylated proteins could be
Figure 23. Chromatographic elution profiles of tryptic peptides from healthy and tumor kidney in three independent RENCA mice. Tryptic peptides obtained by digestion of biotinylated proteins in samples of healthy kidney (left) or tumor kidney (right) from three independently perfused RENCA mice were subjected to microcapillary liquid chromatography-tandem mass spectrometry for protein identification. Chromatographic elution profiles show a good reproducibility of the in vivo biotinylation reaction in different experiments.
131
efficiently captured on streptavidin-Sepharose (SA) resin slurries, even in the
presence of strong anionic detergents, and the bulk of non-biotinylated proteins
washed away by increasingly stringent washing steps (Rybak, Scheurer et al. 2004).
We then performed a digestion step with trypsin while the proteins were still bound to
the SA-Sepharose resin. The resulting tryptic peptides were subjected to
microcapillary liquid chromatography-tandem mass spectrometry for protein
identification. Although a number of peptides from streptavidin could also be detected
(particularly in samples from saline-perfused control mice), this did not compromise
the analysis of peptides from biotinylated organs. Overall, the procedure turned out to
be extremely reproducible. Figure 23 shows the chromatographic profile of tryptic
peptides obtained from biotinylated proteins of healthy kidneys and of tumor bearing
kidneys from three independent RENCA mice.
Strikingly, the profiles exhibit consistent differences among normal and tumor
kidneys. A good reproducibility could also be seen at the level of protein
identification, finding in most cases the same peptides in specimens from different
mice (Supplementary Table 2 in Appendix B). The percentage of proteins
reproducibly identified in three independent mice ranged from 60% in the tumor, to
almost 70% in muscle and kidney, to more than 85% in the liver.
Table 5 shows a list of some of the proteins that could be identified in all tissues, or
solely in one of the organs or tumors. Figure 24 shows an analysis of the subcellular
distribution of the identified proteins, based on their SwissProt database accession
number, in the kidney, liver, skeletal muscle, heart, as well as F9 and RENCA tumors.
Cell surface proteins and extracellular matrix proteins were present in all organs
examined, in a proportion that ranged cumulatively between 20 and about 50%.
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Table 5. Proteins identified in all tissues, or selectively in only one organ or only in tumors
Origin
Protein name Swissprot Accession Number K
idne
y
Live
r
Mus
cle
Hea
rt
F9 tu
mor
REN
CA
tu
mor
Glyceraldehyde 3-phosphate dehydrogenase P16858 + + + + + + L-lactate dehydrogenase A chain P06151 + + + + + + Methylcrotonyl-CoA carboxylase alpha chain Q99MR8 + + + + + + Propionyl-coenzyme A carboxylase, alpha chain Q80VU5 + + + + + + Pyruvate carboxylase Q05920 + + + + + + Serum albumin precursor P07724 + + + + + + Na+K+ transporting ATPase alpha-1 chain Q8VDN2 + Cadherin-16 precursor O88338 + Kidney-specific membrane protein NX-17 Q9ESG4 + Kidney-specific transport protein Q61185 + Na+K+ transporting ATPase alpha-4 chain Q9WV27 + Na+K+ transporting ATPase gamma chain Q04646 + Asialoglycoprotein receptor 1 (Hepatic lectin 1) P34927 + Fatty acid-binding protein, liver P12710 + Glycogen phosphorylase, liver form Q9ET01 + Nebulin Q62411 + Scavenger receptor class B member 1 Q61009 + Actin, alpha skeletal muscle P02568 + Dihydropyridine-sensitive L-type Ca++ channel alpha-2/delta subunits precursor O08532 +
Similar to myosin, heavy polypeptide 2, skeletal muscle adult Q922D2 +
Tenascin X O35452 + Voltage-dependent L-type calcium channel, alpha-1S subunit Q02789 +
Actin, alpha cardiac P04270 + Actinin alpha 2 Q8K3Q4 + ADP,ATP carrier protein, heart/skeletal muscle isoform T1 P48962 + Cardiac Ca2+ release channel Q9ERN6 + Myosin heavy chain, cardiac muscle alpha isoform Q02566 + Myosin-binding protein C, cardiac-type O70468 + Collagen alpha 2(IV) chain precursor P08122 + Glucosylceramidase precursor P17439 + Transmembrane receptor precursor homolog Q8BKG3 + Acetyl-CoA carboxylase 265 (fragment) Q925C4 + + Collagen alpha 1(IV) chain precursor P02463 + Integrin alpha-V precursor P43406 + T-lymphoma invasion and metastasis inducing protein 1 Q60610 + Transketolase P40142 + Vitronectin precursor P29788 +
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Kidney n = 155 12%
24%
3% 19% 1%
17%
14% 10%
Liver n = 151 1% 13%
1%
25%
1%43%
5% 11%
Muscle n = 56
29%
16% 11% 5% 0%
14%
25% 0%
F9 tumor n = 44
18%
5%
7%
11%7% 11%
30%
11%
Heart n = 62
11% 13%
6%
37% 0%
15%
16% 2%
RENCA tumors n = 85
20%
22%5% 15% 0%
11%
21% 6%
ECM
membrane
cytoskelet.
organelles
nucleus
cytoplasm
secreted
unknown, other
Figure 24. Subcellular localization of proteins identified Subcellular localization of the proteins identified in kidney (n=155), liver (n=152), skeletal muscle (n=56), heart (n=62), F9 tumor (n=44) and RENCA tumor (n=85). A legend reporting the correspondence between subcellular compartments and color-coding of the pie chart is shown in the lower part of the panel.
However, cytoplasmic and cytoskeletal proteins were also significantly represented,
as well as blood components, which were particularly abundant in the tumor and
muscle samples.
A few proteins could be identified in the organs and tumors from the control mice
groups, which had been perfused with a saline solution. These proteins (mainly
pyruvate carboxylase, propionyl CoA carboxylase and methylcrotonyl CoA
carboxylase) are enzymes that have multiple covalent binding sites for biotin and use
this vitamin as a cofactor. In muscle of control mice, a few other proteins (isoforms of
myosin and actin) could also be identified.
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3.4.4. Validation of some candidate marker proteins identified
In order to validate the results of the mass spectrometry analysis and confirm the
patterns of expression of the proteins identified, we performed a Western Blot and
immunofluorescence analysis. Proteins extracted from heart, kidney, liver, muscle and
tumors were normalized using the bicinchoninic acid assay and loaded on replicate
gels, separated by SDS-PAGE (Fig 25, panel A), then blotted on nitrocellulose.
Membranes were probed with commercial antibodies against Kidney-specific
A B
Figure 25. Western Blot analysis of organ–specific markers
(A) Equal amounts of heart, kidney, liver, skeletal muscle, F9 and RENCA tumor total protein extracts from a saline–perfused SvEv mouse were separated by SDS–PAGE and stained by Coomassie Brilliant Blue staining. Molecular weight markers are indicated on the left. (B) Other replicate gels were directly transferred to nitrocellulose and a staining of bands immunoreactive towards anti–kidney specific cadherin 16 (anti–Ksp cadherin), anti–calcium channel L–type DHPR alpha 1 subunit, (anti–Cacna1s), anti–calcium channel, voltage gated α2/δ–1 subunit (anti–Cacna2d1) anti–protein tyrosine kinase 7 (anti–PTK7) and anti–T lymphoma invasion and metastasis inducing protein 1 (anti–TIAM1) was performed, followed by incubation with the appropriate secondary antibody conjugated to horseradish peroxidase and ECL detection.
cadherin 16, calcium channel L-type DHPR alpha 1 subunit, calcium channel voltage
gated α2/δ-1 subunit, and two tumor antigens, PTK7 and TIAM1. Figure 25 (panel B)
135
shows that the Kidney-specific cadherin 16 antibody stained a band corresponding to
the expected molecular weight and expressed only in kidney. A similar result was
observed for the two antibodies against calcium channels, which recognized specific
bands only in muscle extracts, and for the antibody directed
against one of the tumor-associated antigens (anti-PTK7), which stained a specific
band of the expected molecular size in both F9 and RENCA tumors, but not in all
other normal organs tested. The antibody directed against TIAM1 stained a band
expressed only in both tumor samples, but cross reacted also with a higher molecular
weight antigen present in all the other normal organs tested.
We also performed an immunofluorescent staining of cryostat sections from vascular
perfusion fixed (3% paraformaldehyde + 0.01% glutardialdehyde) tissue specimens
(Figure 26). The use of secondary antibodies specific for mouse immunoglobulins led
to a faint background staining of the murine tissues (Figure 26, -). However the use of
primary monoclonal antibodies in the immunofluorescence procedure resulted in a
specific staining, as clearly visible in the kidney sections stained with the kidney-
specific cadherin 16 antibody and the muscle sections stained with the calcium
channel antibody (Figure 26, +). Kidney-specific cadherin 16 (Thomson, Igarashi et
al. 1995) is a member of the cadherin superfamily of cell adhesion molecules, whose
expression in the kidneys is confined to the basolateral membranes, facing the
intercellular spaces, of tubular epithelial cells (Thomson and Aronson 1999). During
the in vivo perfusion procedure, these structures are accessible to the biotinylation
reagent through the small fenestrated peritubular capillaries (Figure 26, upper left
panel).
In cross section of skeletal muscle (Figure 26, lower left panel) the anti calcium
channel L type DHPR alpha 1 subunit antibody related-immunostaining is seen in the
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+ -
Figure 26. Immunofluorescence detection of organ–specific markers Cryostat sections from mice kidney and skeletal muscle, fixed with 3% PFA + 0.01% GA by vascular perfusion; the binding sites of the primary mouse antibody are revealed by FITC–conjugated anti mouse IgG. Nuclear DNA is stained by DAPI, added to the secondary antibody. In the kidney (upper panel, +) ksp–cadherin 16 is seen in lateral cell membranes (green staining) bordering the intercellular spaces between the tubular epithelial cells. The extent of lateral cell membranes varies among nephron segments. Cell nuclei are shown in red. In cross section of skeletal muscle (lower panel, +) the anti calcium channel L type DHPR alpha 1 subunit antibody related–immunostaining is seen in the transverse tubule system, surrounding the muscle fibers and in a fine punctuate pattern within the fibers. In negative controls (–) the primary antibody was omitted. The weak background staining of the basement membranes is due to the cross reaction of the secondary anti mouse antibody with endogenous mouse IgG. Bars = 50 µm.
transverse tubule system, surrounding the muscle fibers and in a fine punctuate
pattern within the fibers.
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3.4.5. Discussion
A number of laboratories, including ours, have experimentally demonstrated (in
animal models and in patients with cancer) that monoclonal antibodies specific to
tumor-associated vascular antigens can be useful for the diagnosis and therapy of
cancer and possibly of other angiogenesis-associated diseases (Bicknell 2002; Carver
and Schnitzer 2003; Brack, Dinkelborg et al. 2004; Thorpe 2004). Future advances in
this field will crucially rely on the identification of vascular antigens, which are
preferentially expressed in certain pathological conditions. Furthermore, a proteome-
wide analysis of accessible proteins may increase our understanding of the dynamic
nature of the endothelium in physiological processes.
Until now, the search for tumor-associated vascular antigens has mainly relied on
serendipitous discovery (Zardi, Carnemolla et al. 1987; Liu, Moy et al. 1997;
Carnemolla, Castellani et al. 1999), on immunization strategies (Buhring, Muller et al.
1991), on transcriptomic analysis of tumor-derived endothelial cells (St. Croix, Rago
et al. 2000; Wyder, Vitaliti et al. 2000) and on bioinformatics approaches (Gerritsen,
Soriano et al. 2002; Huminiecki, Gorn et al. 2002). Furthermore, the groups of
Ruoslahti and Pasqualini have pursued the in vivo panning of peptide phage libraries
for the discovery of “signature peptides” capable of identifying vascular structures in
normal organs and in tumors (Pasqualini and Ruoslahti 1996). A comparatively
smaller number of proteomic investigations have also been performed, hampered by
the difficulty to recover, by cell purification (Alessandri, Chirivi et al. 1999) or by
laser capture microdissection (Craven, Totty et al. 2002), a sufficient amount of pure
endothelial cells either from tumor or from normal vasculature.
138
In principle, the most direct way to assess differences in protein abundance between
the tumor neo-vasculature and normal vasculature would consist in the in vivo
chemical labeling of accessible structures (as described in this thesis) or in the
physical separation of surface proteins [as pioneered by Jan Schnitzer (Jacobson,
Schnitzer et al. 1992; Durr, Yu et al. 2004)], followed by a rapid recovery and
comparative proteomic analysis of the two samples. De la Fuente et al. have described
the artificial ex vivo perfusion of lungs isolated from normal and hyperoxic rats with
sulfo-NHS-LC-biotin (De La Fuente, Dawson et al. 1997). After SDS-PAGE, the
biotinylated proteins were visualized using a chemiluminescence substrate for the
streptavidin-horseradish peroxidase conjugate, outlining differences in rats exposed to
hyperoxia for 48-60 hours (De La Fuente, Dawson et al. 1997). However, the
procedure did not allow protein identification.
Our method offers a number of potential advantages compared to other methods
aimed at characterizing accessible proteins in normal tissues and in sites of disease.
First, unlike 2D-gel-based techniques, our approach is compatible with the use of
strong anionic detergents (e.g., SDS), which are indispensable for the solubilization of
most membrane proteins and extracellular matrix components during the critical
phase of protein extraction from the tissues. Second, the chemical properties of the
reactive organic molecule used in the perfusion reaction can be changed, in order to
influence the structures, which are accessible, in vivo and which can be labeled and
recovered. Indeed, it should be possible to choose labeling reagents, which mimic the
pharmacokinetic properties of the targeting agents that one intends to use for imaging
or therapeutic applications. For example, we can alter the charge, size (e.g., by
conjugation to dextran) and lipophilicity of the reactive derivatives of biotin. Indeed,
139
the approach is not limited to biotin, and any other reactive molecule for which a
high-affinity reagent is available could be considered (e.g., fluorophores and the
corresponding antibodies). Third, the proteomic analysis described in this thesis is
easy to implement, sensitive, and allows the identification of hundreds of different
accessible proteins in the tissues of interest. The method is biased towards the most
abundant antigens, which are also the preferred ones for ligand-based tumor targeting
applications (Brack, Dinkelborg et al. 2004). However, advances in mass
spectrometry (Geoghegan and Kelly 2004) or the use of enzyme-specific labeling
methods (Adam, Sorensen et al. 2002) may facilitate the identification of less
abundant tissue specific antigens.
In our analysis, a number of proteins were found to be biotinylated in several tissues
(e.g., glyceraldehyde 3-phosphate dehydrogenase, collagen type VI (alpha 3), actin;
Supplementary Table 1). In addition, a number of previously known organ-specific
antigens were found to be expressed only in the expected organ (e.g., kidney
transporters, muscle and cardiac channels; Table 5 and Appendix B). Interestingly, in
a given organ we were able to identify both organ-specific proteins [e.g., kidney
specific transmembrane protein 27 (Zhang, Wada et al. 2001), kidney specific
transport protein (Lopez-Nieto, You et al. 1997) and Na+K+ transporting ATPase
gamma chain (Forbush, Kaplan et al. 1978)] and proteins which are known to be
expressed ubiquitously (e.g., Na+K+ transporting ATPase alpha-1 chain in kidney).
The fact that these last proteins could be identified only in one organ might reflect
either a difference in their relative abundance among different organs, or a facilitated
accessibility in that particular organ, or both. Moreover, some hypothetical proteins
140
and proteins of unknown function could also be specifically identified in different
organs.
The results obtained by in vivo biotinylation can differ in some cases from previous
transcriptomic studies of genes expressed in human tumor endothelium. For example,
St. Croix et al. (St. Croix, Rago et al. 2000) described collagen type IV (alpha 2) as a
pan-endothelial marker, while presented collagen type VI (alpha 3), nidogen and
collagen type I as genes up-regulated in angiogenic vessels. In our analysis, collagen
type VI and nidogen were found in various normal organs as well as in tumors, while
collagen type IV was preferentially found in both tumors studied. However, St. Croix
and colleagues analyzed endothelial cells from colorectal tumors and normal mucosa
specimens, while we studied mouse organs and transplanted tumors, in which
basement membrane is more abundant. Indeed, a number of studies have shown that
collagen type IV is abundantly expressed by basal lamina in solid tumors. In clear cell
type renal carcinomas, Droz and colleagues found collagen type IV (alpha 1) and
collagen type IV (alpha 2) in basal laminae surrounding tumor islets (Droz, Patey et
al. 1994). Histochemical studies performed on tissues from 11 patients showed that
collagen type IV was always present in the basement membrane surrounding tumor
nests of basal cell carcinoma, although tumors with different degrees of invasivity
showed a different distribution of the collagen type IV chains (Tanaka, Iyama et al.
1997). Confocal microscopic studies (Baluk, Morikawa et al. 2003) have shown that
basement membrane (of which collagen type IV is the major constituent) covered
>99.9% of the surface of blood vessels in the three different tumor types examined
(Baluk, Morikawa et al. 2003). Our results could reflect a preferential accessibility of
collagen type IV in tumor basement membrane, due to the increased fenestration of
tumor blood vessels (Maeda, Fang et al. 2003).
141
Some of the antigens identified exclusively in tumor specimens have already been
shown to be activated or over expressed in solid tumors or in in vitro tumor cell lines.
For example, tumor tissues from patients with a histopathological diagnosis of
glioblastoma multiforme or anaplastic astrocytoma were analyzed for activity of the
lysosomal enzyme glucosylceramidase (and other lysosomal enzymes) which was
found to have an increased activity, as compared with that in normal brain tissue
(Nygren, von Holst et al. 1997).
A vast literature has demonstrated an association between integrin alpha V over
expression and tumor cell proliferation (Levinson, Hopper et al. 2002; Cruet-
Hennequart, Maubant et al. 2003) or tumor propensity to metastasize to distal organs
(Pecheur, Peyruchaud et al. 2002). Human ovarian OV-MZ-6 cancer cells express the
integrin alpha(v)beta3, which associates with vitronectin and correlates with ovarian
cancer progression (Hapke, Kessler et al. 2003). Our experiments show a selective in
vivo biotinylation of alphaVβ3 integrin in tumors, mirroring magnetic resonance
imaging results obtained targeting a paramagnetic contrast agent to endothelial
alphaVβ3 in rabbit carcinomas through a specific monoclonal antibody (Sipkins,
Cheresh et al. 1998). Indeed, alphaVβ3 integrin has been proposed as a marker of
tumor angiogenesis (Auerbach and Auerbach 1994; Kumar 2003) and a humanized
monoclonal antibody to alphaVβ3 (Vitaxin) (Gutheil, Campbell et al. 2000) is in phase
II clinical trials as a therapeutic agent for blocking tumor-induced angiogenesis in
melanoma and prostate cancer (Tucker 2003).
Transketolase, a thiamine diphosphate-dependent enzyme linking the nonoxidative
branch of the pentose phosphate pathway to the glycolytic pathway, has been shown
to control in vivo tumor growth in mice with Ehrlich’s ascites tumor (Comin-Anduix,
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Boren et al. 2001), and to be over expressed in highly metastatic adenoid cystic
carcinoma cell lines compared with their poorly metastatic counterparts (An, Sun et
al. 2004). Studies reported in the Anatomical Viewer of the SAGE Genie website
(Boon, Osorio et al. 2002) (http://cgap.nci.nih.gov/SAGE) show that transcripts
coding for transketolase are abundant only in white blood cells of the healthy adult.
By contrast, high levels of mRNAs for this enzyme can be found in lung, kidney and
colon tumor tissues.
Finally, acetyl CoA carboxylase isozymes have been reported to show distinctive
tissue distribution and regulation. The polypeptide of Mr 265,000 (Acetyl CoA
carboxylase 265) is the sole form expressed in white adipose tissue, while the isozyme
Acetyl CoA carboxylase 280 predominates in cardiac and skeletal muscle (Thampy
1989; Bianchi, Evans et al. 1990; Iverson, Bianchi et al. 1990; Louis and Witters
1992). Both forms are present in liver, brown adipose tissue, lactating mammary
gland and pancreatic islets (Thampy 1989; Bianchi, Evans et al. 1990; Iverson,
Bianchi et al. 1990; Louis and Witters 1992). Recently, acetyl CoA carboxylase 265
has been identified as a partner of the protein encoded by the breast cancer
susceptibility gene BRCA1 (Magnard, Bachelier et al. 2002). Our studies show that
acetyl CoA carboxylase 265 is either more abundant and/or more easily accessible in
the tumors tested, than in other normal tissues, thus suggesting its use as antigen for
ligand-based tumor targeting applications.
A couple of other proteins, specifically identified in tumors, have not yet been
extensively characterized as tumor markers and represent interesting candidates for
development of targeting antibodies. We identified the murine homolog of the human
PTK7 protein. PTK7 is a receptor protein tyrosine kinase-like molecule that lacks
143
detectable activity of the tyrosine kinase domain, but appears to have a role in signal
transduction. In a high-throughput analysis of receptor tyrosine kinase expression in
human cancers, using quantitative real-time RT-PCR, protein tyrosine kinase 7
(PTK7) was shown to be highly over expressed in acute myeloid leukemia samples
(Muller-Tidow, Schwable et al. 2004). Jung and colleagues identified four additional
splicing variants of PTK7 and could show that two of them are preferentially
expressed both in testis and in hepatoma and colon cancer cell lines, but not in normal
embryonic fibroblasts (Jung, Ji et al. 2002). This line of evidence suggests that, in
analogy with other genes undergoing a specific pattern of alternative splicing in
tumors, PTK7 isoforms could be differently distributed in tumor tissues, compared to
their normal counterparts. We are presently raising monoclonal antibodies against
distinctive domains of the five different isoforms of PTK7, aiming at a
characterization of their expression in normal and tumor specimens.
The role of the guanine nucleotide exchange factor Tiam1 in tumor progression has
been investigated in different tumor models (Minard, Kim et al. 2004). Recent results
demonstrated that increased Tiam1 expression correlates with grade of breast cancer
in humans and metastatic potential of human breast carcinoma cell lines in nude mice
(Adam, Vadlamudi et al. 2001). Our Western blot results point to a different
processing of the Tiam1 protein in tumor samples, as compared to normal tissues
tested. Indeed, a specific cleavage of Tiam1 by caspases has been shown to occur in
multiple cell lines, as a response to different apoptotic stimuli, including ceramide
(Qi, Juo et al. 2001). It is not clear whether, in our hands, processing of TIAM in
normal tissues and tumor tissues depends on the activation of specific apoptotic
pathways or on aspecific cleavage occurring after cell lysis. The fact that the same
144
antibody recognizes different forms of Tiam1 in normal tissues and tumors, however,
grants further investigations to characterize the nature and function of these different
TIAM1 forms.
A non-negligible fraction of identified proteins was intracellular proteins or serum
components (particularly in tumors). Sulfo-NHS-LC-biotin has been shown in in vitro
studies to be capable of passing through cell membranes and to label intracellular
proteins, in addition to surface proteins (Peirce, Wait et al. 2004), while disulfide-
linked biotin derivatives yielded a more specific labeling of membrane proteins, due
to disulfide bond cleavage in the reducing intracellular milieu. Interestingly, in vivo
biotinylation studies performed in our group with sulfosuccinimidyl 2-(biotinamido)-
ethyl-1,3-dithiopropionate (sulfo-NHS-SS-biotin) did not lead to improved recovery
of membrane proteins, possibly due to in vivo instability (unshown results). Serum
components were observed at higher frequency in solid tumors. Blood coagulation
products have long been known as components of a provisional stroma which sustains
tumor cell growth (Dvorak 1986), but may also reflect transient thrombotic events in
some tumor blood vessels or insufficient perfusion of the tumor mass.
One of the most exciting applications of our technology resides in the combination of
protein biotinylation and ex vivo perfusion procedures. A number of tumor-bearing
organs (e.g., kidney, colon) are readily available from surgical procedures and will
allow the proteomic study of human tumor vasculature. In vivo and ex vivo
biotinylation procedures are not limited to the study of tumor biology, but can be
applied to several other pathologies (e.g., atherosclerosis, aneurisms, chronic
inflammatory conditions), as well as to the study of basic physiological and
145
immunological processes. The rapid identification of antigens will have to be
complemented by the rapid verification of the patterns of expression in tissues, either
by in situ hybridization or (preferably) by immunohistochemistry. Methodologies for
the rapid production of monoclonal antibodies are likely to play a crucial role in
future developments of this field of research (Winter, Griffiths et al. 1994;
McCafferty and Glover 2000; Elia, Silacci et al. 2002).
Finally, it is worth mentioning that our in vivo biotinylation procedure will
chemically modify accessible amine-containing phospholipids (such as
phosphatidylserine in tumor blood vessels (Ran, Downes et al. 2002; Ran and Thorpe
2002)) and certain oligosaccharides. Improved analytical methodologies are badly
needed to allow a comprehensive description of in vivo heterogeneity of these classes
of biomolecules.
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4. Conclusions and Outlook
4.1. Conclusions
A broad-range investigation of gene expression modulation, induced by exposure of
fibroblasts to an acidic milieu or exposure to hypoxia of endothelial cells, confirmed
the orchestrated, adaptation response of these cells to adverse environmental
conditions, similar to those normally present in large solid tumors. This adaptive
response resulted in an up-regulated expression of downstream genes, many of which
are involved in control of cell cycle progression, cellular metabolism, matrix
remodeling and activation of neoangiogenesis. However, a number of genes, for
which a pH- or oxygen-dependent transcription regulation was not previously known,
have also been found to be up regulated in our experiments. For some genes, the up-
regulation of expression found in hypoxic endothelial cells overlaps to the up-
regulated expression registered in normal fibroblasts upon starvation or medium
acidification (stanniocalcin, tetranectin), pointing to a possible role for their functional
products in the tumor micro-environment. Other genes (Del-1), with a more restricted
pattern of expression, promise to become interesting markers of tumor angiogenesis
and targets for therapeutical intervention.
Finally, the in vivo biotinylation approach outlined the importance of a new parameter
to be considered, besides abundance and stability, in the search for better markers of
pathology: accessibility. Differences in the permeability of the vascular network in
tumors, as compared to normal organs, may account, at least in part, for the
differential exposure of a number of proteins, otherwise known to be ubiquitously
present in many bodily compartments, to chemical agents present in the bloodstream.
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The nature of the chemical agents themselves, in turn, may determine their in vivo
distribution, and be shaped in a way to influence the class of proteins with which
these agents will ultimately interact. In an effort to broaden the panel of biotinylating
agents available and ameliorate their performance, we are in the process of
synthesizing new chemical derivatives of biotin (e.g., containing a cleavable linker
resistant to naturally occurring reducing agents or containing phosphate groups
instead of sulphonate groups) aiming in particular at improving the selective labeling
of membrane proteins.
The relevance to physiological and pathological processes of our findings deserves
further investigation and validation. We are currently producing recombinant domains
of some of the more promising candidate targets identified. These antigens will be
used in panning experiments with our antibody-phage library ETH-2 Gold (Silacci,
Brack et al. 2005), in order to isolate antibody binders (in scFv format) to be used in
immunohistochemistry and in vivo biodistribution experiments.
4.2. Outlook: ex vivo protein biotinylation of surgical specimens from tumor-
bearing patients.
A natural evolution of the in vivo biotinylation technique presented above consists in
its ex-vivo application, namely the perfusion with biotinylating reagents of surgical
specimens (ideally, intact organs) removed from tumor-bearing patients. The aim of
this study is the identification of tumor markers which are specifically accessible from
the blood, and which are absent or less accessible in other normal tissues. These
tumor-specific marker proteins could be used as targets for the delivery of diagnostic
and therapeutic agents to the tumor-site.
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The method used in this study, exemplified for a human tumor-bearing kidney in
Figure 27, relies on the perfusion of surgically removed organs from tumor bearing
Tumor tissue
Blood vessel
Ex vivoperfusion of a tumor-bearing human kidney
Extraction (SDS) and purification of biotinylatedproteins on SA-Sepharose
Comparative proteomic analysis Target
identification
Biotinylation of accessible proteins
O
S
NHHN
O
S
NHHN
O
SNH
HN
O
SNH
HN
O
SNH
HN
O
SNH
HN
O
SNH
HN
O
S
NHHN
O
S
NHHN
O
S
NHHN
Elution
Tumor
Tumor tissue
Blood vessel
Ex vivoperfusion of a tumor-bearing human kidney
Extraction (SDS) and purification of biotinylatedproteins on SA-Sepharose
Comparative proteomic analysis Target
identification
Biotinylation of accessible proteins
O
S
NHHN
O
S
NHHN
O
S
NHHN
O
S
NHHN
O
SNH
HNO
SNH
HN
O
SNH
HNO
SNH
HN
O
SNH
HNO
SNH
HN
O
SNH
HNO
SNH
HN
O
SNH
HNO
SNH
HN
O
S
NHHN
O
S
NHHN
O
S
NHHN
O
S
NHHN
O
S
NHHN
O
S
NHHN
Elution
Tumor
Figure. 27. Schematic overview of the ex vivo biotinylation of tumor-bearing human kidneys.
patients with a solution containing a biotinylation reagent. We plan to use a charged
active ester derivative of biotin (sulfo-NHS-LC-biotin) with an impaired diffusion
through biological membranes (Dentler 1995), as described in other parts of this
thesis, but other reactive biotin derivatives can also be considered at later stages of the
study. As a result, proteins with accessible primary amino groups and certain amine-
149
containing glycolipids and phospholipids will be covalently modified with biotin. A
differential proteomic analysis of biotinylated proteins in specimens of the diseased
portion of the organ under study, as compared to specimens of a healthy portion of the
organ will reveal accessible tumor marker candidates. After their further validation
and production of specific binders (e.g., human monoclonal antibodies), these targets
might help to improve the diagnosis and/or therapy of cancer.
The perfusion with biotinylation reagent, which has already been successfully applied
to tumor-bearing mice in vivo (Rybak, Scheurer et al. 2004; Scheurer, Rybak et al.
2004; Rybak, Ettorre et al. 2005), is presently being applied in a modified perfusion
protocol to the ex vivo biotinylation of kidneys from rats.
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5. Materials and Methods
5.1. Cell culture
5.1.1. NHDF cell culture
Cryopreserved primary normal human dermal fibroblasts (NHDF,
Clonetics/BioWhittaker, Verviers, Belgium) were cultured in DMEM supplemented
with 10% fetal bovine serum (FBS) and 1x antibiotics/antimycotics (all from
GibcoBRL, Life Technologies, Paisley, UK) in a 5% CO2 atmosphere at 37°C. Cells
were seeded at 3000 cells/cm2 and the medium was changed daily (1 - 2 mL per
5 cm2). After reaching confluence, the monolayer was washed with sterile PBS and
then either incubated in serum-free medium (for proteomic and transcriptomic
experiments) or in complete medium (transcriptomic), buffered at two different pH
values (6.7 and 7.5) (Viti, Bumke et al. 2000). The different pH values of the medium
were obtained by adding different concentrations (4 to 20 mM) of sodium bicarbonate
to bicarbonate-free DMEM (both from GibcoBRL). To further eliminate serum
proteins and proteins synthesized before changing the pH in the proteomic
experiments, medium was changed once again after 24 h and replaced with fresh
serum-free medium at the two different pH values. A daily measurement was
performed in order to confirm that the pH of the medium remained constant
throughout the whole experiment duration. After 72 hours incubation at pH 6.7 or 7.5
in a 5% CO2 atmosphere at 37°C, the medium containing the proteins secreted by the
fibroblasts was collected and concentrated up to 800-fold in a Vivaspin Concentrator
(20 mL MWCO 10000, Vivascience, Sartorius, Göttingen, Germany) and used for 2D
gel electrophoresis. For transcriptomic experiments, the cells were collected after 24,
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48 or 72 hours incubation in medium buffered at pH 6.7 or 7.5, trypsinized and used
for RNA extraction.
5.1.2. HUVEC cell culture
Cryopreserved primary human umbilical vein endothelial cells (HUVEC) pooled from
several donors were from Clonetics (BioWhittaker, Verviers, Belgium). The cells
were cultured in Endothelial Cell Growth Medium EGM-2 (BioWhittaker, containing
growth factors) in a 5% CO2 atmosphere at 37°C. The cells were seeded at 2500
cells/cm2 and the medium was changed daily (1 - 2 mL per 5 cm2) until harvesting the
For the transcriptomic experiment, triplicate, independent samples were obtained.
HUVECs were grown in complete EGM-2 medium at pH 7.4 with HEPES until about
80% of confluence. At that point, replicate flasks were washed and the medium
replaced with fresh EGM-2, in the presence of 10% FBS, and incubated for either 12,
24 or 48 hours in hypoxic conditions (2% oxygen, v/v) or for 48 hours in normoxia.
All along the experimental time course, cells did not change morphology, and for all
conditions, cell mortality was constantly below 5%. At the different time points, total
RNA was extracted by means of the RNeasy mini kit (Qiagen, Valencia, CA, USA)
and its quality monitored by 1% agarose electrophoresis. Only RNAs that did not
show smears in this test were used for further analysis.
5.1.3. HUVEC and HEK cell culture for 2D peptide mapping experiments
Cryopreserved primary HUVEC pooled from several donors were obtained from
Clonetics (BioWhittaker, Verviers, Belgium). The cells were cultured in Endothelial
Cell Growth Medium EGM-2 (BioWhittaker), containing growth factors in a 5% CO2
atmosphere at 37°C. The cells were seeded at 2500 cells/cm2 and the medium was
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changed every other day (1 - 2 mL per 5 cm2) until harvesting the cells. HUVEC were
grown in EGM-2 medium at pH 7.4 with HEPES (Sigma-Aldrich, St. Louis, MO)
until about 80% of confluence. At that point, replicate flasks were washed and the
medium replaced with fresh EGM-2 and incubated either for 48 hours in hypoxic
conditions (2% oxygen, v/v) or for 48 hours in normoxia. During the whole
experimental time course, cells did not change morphology, and for all conditions,
cell mortality was constantly below 5%.
Cryopreserved HEK cells were cultured in Dulbecco’s Modified Eagle Medium
(DMEM, Invitrogen Gibco, Carlsbad, CA) containing fetal bovine serum (Invitrogen
Gibco) and antibiotics (Invitrogen Gibco) in a 5% CO2 atmosphere at 37°C. Cells
were seeded at 38’000 cells/cm2 in cell culture flasks containing 1 ml of growth
medium per 5 cm2, which was replaced by the same volume of fresh medium after 24
hours. The cells reached confluence 4 days after seeding.
5.1.4. F9 teratocarcinoma cells and RENCA cells for in vivo biotinylation
experiments
The murine nonmetastatic teratocarcinoma cell line (F9B9) was purchased from
American Type Culture Collection (Manassas, VA). F9B9 were cultured in DMEM
containing fetal bovine serum and antibiotics in a 5% CO2 atmosphere at 37°C. Cells
were seeded at a density of approximately 80,000 cells/cm2 in a 75 cm2 tissue culture
flask containing 15 ml of growth medium. After 24 hours, the cells were sub cultured
in a ratio 1:4 using trypsin (Invitrogen, Gibco) and growth medium as subculture
reagents. Sub culturing was repeated twice before using the cells for subcutaneous
injection.
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The RENCA cells were maintained as monolayer cultures in modified Eagle’s
medium supplemented with 10% fetal bovine serum, vitamin solution (Gibco, Grand
Island, N.Y.) and incubated in a 5% CO2 atmosphere at 37°C.
5.2. Transcriptomic methods
5.2.1. Northern blot
RNA samples (~10 µg) were subjected to electrophoresis on a 1% w/v agarose gel
containing 6.5% formaldehyde, 20 mM 3-morpholinopropane sulfonic acid, 8 mM
sodium acetate, 1 mM EDTA. RNA Ladder High Range (MBI Fermentas, Labforce,
Nunningen, Switzerland) was used as molecular weight marker. The gel was run for
2.5 h at 120 V, 120 mA.
The separated RNA was then blotted overnight onto Hybond-N+ nylon membrane
(Amersham Biosciences, Little Chalfont, UK) by capillary transfer using 10x SSC
buffer (1.5 M NaCl, 0.15 M sodium citrate; Roche, Basel, Switzerland). The RNA
was cross linked to the nylon membrane using an UV Stratalinker 2400 (Stratagene,
Basel, Switzerland) at 120 mJoules. The probe employed to visualize the tenascin-C
large isoform-coding mRNA was the HT-2 probe, described in (Borsi, Carnemolla et
al. 1992), radioactively labeled with α-32P dCTP (Redivue, Amersham Biosciences)
and the Rediprime II random prime labeling system (Amersham Biosciences).
5.2.2. Oligonucleotide microarray analysis
For transcriptomic analysis, the human HG-U95A ver2 (for NHDF study) or the
human HG-U133A (for HUVEC study) oligonucleotide microarrays (Affymetrix,
Santa Clara, CA, USA) were employed. Samples to be analyzed were prepared
154
following the manufacturer’s protocols (Lipshutz, Fodor et al. 1999; Lipshutz 2000).
For each experimental condition, three independent replicate samples were obtained.
Briefly, cells were washed twice with cold PBS, trypsinized, harvested and washed
once again with PBS. Total RNA was extracted by means of the RNeasy mini kit
(Qiagen, Valencia, CA, USA) and its quality monitored by 1% agarose
electrophoresis. Only RNAs, which did not show smears in this test, were used for
further analysis.
Twenty micrograms of total RNA were retro transcribed using an HPLC-purified T7-
(dT)24 primer, to yield a first strand cDNA. In turn, this served as a template for the
synthesis of the complementary second strand. The synthesis of this double-stranded
cDNA was carried out by making use of the SuperScript cDNA synthesis customer kit
(Invitrogen, Groningen, The Netherlands). After cleaning up, ethanolic precipitation,
and resuspension, an aliquot of the cDNA was used for the in vitro transcription
reaction. This last was performed by means of the MEGA Script T7 in vitro
transcription kit (Ambion, Austin, TX, USA), including biotinylated ribonucleotides
in the reaction mixture (biotinyl-11-CTP and biotinyl-16-UTP, Enzo New York, NY,
USA). After cleaning of the in vitro transcription reaction products (RNeasy, Qiagen),
15 micrograms of the biotinylated cRNA were heat-fragmented (95°C, 35 min.) and
hybridized overnight to the oligonucleotide microarray at 45°C in a rotary oven.
The final washing, staining and scanning steps were performed using the Affymetrix
GeneChip fluidic station and Agilent GeneArray scanner (Agilent, Palo Alto, CA,
USA), following manufacturer’s instructions (Lipshutz, Fodor et al. 1999; Lipshutz
2000).
The analysis of absolute expression levels for the genes represented on the chips was
performed with the Affymetrix Micro Array Suite 2.0 software. Scaling and
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normalization procedures were performed in order to enable the comparison of results
obtained in different experiments. The comparative analysis of results obtained for the
different cell growth conditions was performed using the Silicon Genetics GeneSpring
version 4.2.1 software (Silicon Genetics, Redwood City, CA, USA). For each
experimental time point, the results arising from the three replicate, identically treated
samples were first averaged. The resulting averages were used to calculate (for each
gene and experimental condition) ratios relative to the average of the expression
levels of the same gene in control samples (NHDF grown at pHo 7.5 for 24 hours or
HUVEC grown in normoxic conditions for 48 hours, respectively). Genes whose
expression was not modulated by more than a factor two (either as an increase or as a
decrease) were not displayed in the figures.
5.2.3. RT-PCR analysis of alternatively spliced transcripts.
Total RNA was extracted from triplicate samples of HUVECs grown in hypoxia or
normoxia for 48h, as described above. RNA concentration was determined by
measuring the absorbance at 260 nm and its quality monitored by the ratio of
absorbance at 260/280 nm and by 1% agarose gel electrophoresis. Reverse
transcription-polymerase chain reaction was performed by means of the Access RT-
PCR System kit (Promega, Madison, WI). RT-PCR conditions were as follows:
reverse transcription at 48°C for 45 min, denaturation at 95°C for 2 min, followed by
cycles (for details, see the Result Section) of denaturation at 94°C for 30 seconds,
annealing at 60°C for 60 seconds and elongation at 68°C for 90 seconds. A final step
of elongation at 68°C for 7 min was performed at the end.
Primers employed in this study are listed in Table 6.
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Table 6
Primers employed for the semi-quantitative RT-PCR analysis
Gene or alternatively spliced domain
Primer sequence Name GenBank #
Position
Del 1 major transcript 5’-CAACTGTTCTAGTGTTGTGGA-3’ Del1for U70312 281-301 Del 1 major transcript 5’-CTTCACTTATTTCACAGGTTC-3’ Del1rev U70312 370-390 Del1 Z20 splice variant 5’-CAACTGTTCTAGTGTTGTGGA-3’ Del1for U70313 171-191 Del1 Z20 splice variant 5’-CTTCACTTATTTCACAGGTTC-3’ Del1rev U70313 230-250 Fibronectin ED-B type III homology domain
5’-CCCCAACTCACTGACCTAAGCTTTG-3’ FN-ED/Bfor X07717 1380-1404
Fibronectin ED-B type III homology domain
5’-CCGTTTGTTGTGTCAGTGTAGTAGG-3’ FN-ED/Brev X07717 1623-1647
Fibronectin ED-A type III homology domain
5’-TGATCGCCCTAAAGGACTGGCATTC-3’ FN-ED/Afor X07718 1249-1263
Fibronectin ED-A type III homology domain
5’-TGGACTGGGTTCCAATCAGGGGCTG-3’ FN-ED/Arev X07718 1487-1511
Fibronectin (type III homology domain 2-4)
5’-TTGTGGCCACTTCTGAATCTGTGAC-3’ FN1for X02761 2082-2106
Fibronectin (type III homology domain 2-4)
5’-TCAGGGGGTACTTGGAGACAGAGGG-3’ FN1rev X02761 3056-3080
Tenascin C FN-type III repeat C
5’-CCCTGCCCCTTCTGGAAAACCTAAC-3’ TNCCfor M55618 4718-4742
Tenascin C FN-type III repeat C
5’-TGTAACAATCTCAGCCCTCAAGGGC-3’ TNCCrev M55618 4962-4986
Tenascin C FN-type III repeat D
5’-CGAACCGGAAGTTGACAACCTTCTG-3’ TNCDfor M55618 4992-5016
Tenascin C FN-type III repeat D
5’-GTTGCTATAGCACTGACTGTCTGGG-3’ TNCDrev M55618 5231-5255
Tenascin C (FN-type III repeats 6-7)
5’-GGGCTCCCCAAAGGAAGTCATTTTC-3’ TNCfor M55618 5265-5289
Tenascin C (FN-type III repeats 6-7)
5’-TCGCCTGTGTAGGAGATGACATAAC-3’ TNCrev M55618 5618-5642
Glyceraldehyde-3-phosphate dehydrogenase
5’-TGAAGGTCGGAGTCAACGGATTTGGT-3’ GAPDHfor M33197 71-86
Glyceraldehyde-3-phosphate dehydrogenase
5’-CATGTGGGCCATGAGGTCCACCAC-3’ GAPDHrev M33197 1030-1053
The products of the RT-PCR reaction were analyzed either by 1% agarose gel
electrophoresis or by PAGE on 20% NovexTM TBE gels (Invitrogen, Carlsbad, CA),
stained with Sybr Green I (1:10,000 diluted in TBE, 30’ in the dark; Molecular
Probes, Eugene, OR) and imaged in the DIANA III fluorescence imager (Raytest,
Straubenhardt, Germany).
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5.3. Gel-based proteomic methods
5.3.1. 2D-PAGE
Samples (prepared as described above) diluted to 350 µl with rehydration solution,
were applied to the 18 cm IPG strips (immobilized pH gradient, ranges pH 3-10
NonLinear, Amersham Biosciences). These were then covered with mineral oil.
Rehydration and isoelectric focusing (IEF) were performed in the IPGphor apparatus
(Amersham Biosciences) at 20°C, max. 80 µA per strip, according to the following
program: 4 h at 0 V, 7 h at 30 V (rehydration), 1 h at 200 V, 1 h at 500 V, 1 h at
1000 V, then 8-12 h at 8000 V (IEF) until reaching 60-100 kVh. IEF markers from
Serva (Heidelberg, Germany) were separated using similar conditions.
After IEF, the IPG strips were equilibrated twice for 20 min with gentle shaking in
10 mL of a solution containing 50 mM Tris-HCl (pH 6.8), 6 M urea, 30% glycerol,
2% w/v SDS, and a trace of bromophenol blue. Two percent (w/v) dithioerythritol
(Sigma) was added to the first, 2.5% w/v iodoacetamide (Sigma) to the second
equilibration step.
For the SDS-PAGE second dimension separation, the IPG strips were placed on top of
the SDS gel and sealed with 0.5% w/v agarose in SDS electrophoresis buffer (25 mM
Tris, 192 mM glycine, 0.1% w/v SDS). The unstained, broad range Precision Protein
StandardsTM from BioRad was used as a molecular weight marker. The
polyacrylamide gels (250 mm x 200 mm x 1.5 mm, 9 - 16.5% T linear gradient)
contained piperazine diacrylamide (BioRad) as cross-linker, 375 mM Tris-HCl pH 8.8
and 0.1% w/v SDS.
Electrophoresis was performed at 15°C in the Hoefer DALT multiple vertical
electrophoresis unit with the 3000Xi power supply (BioRad), according to the
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following program: 2 h at 50 V, 2 h at 100 V, then at 150 – 200 V overnight until the
bromophenol blue front had migrated to the end of the gel.
For fluorescent staining with SYPRO Ruby, compatible with subsequent tryptic
digestion, the gels were washed in H2O, fixed in 7% acetic acid, 10% methanol for
30 min, and then stained overnight in SYPRO Ruby protein gel stain (Molecular
Probes). To decrease background fluorescence, the gels were destained in 7% acetic
acid, 10% methanol for 30 min before imaging. The gels were imaged with a DIANA
III fluorescence imager (Raytest).
Differences in spot intensities in the different conditions tested were quantitatively
evaluated by means of the software package ProteomWeaver 2.1.0 (Definiens AG,
Munich, Germany).
5.3.2. In-gel tryptic digestion of proteins
Protein spots were excised from the SYPRO Ruby-stained 2D-PAGE gels, transferred
to a 96-well plate and washed twice with 100 mM ammonium bicarbonate (Sigma).
The gel spots were dehydrated with acetonitrile and dried in a SpeedVac concentrator
SC110 (Savant, Fisher Scientific, Pittsburgh, PA, USA). The dry gel spots were
rehydrated with 50 mM ammonium bicarbonate buffer containing 12.5 ng/µL
sequencing grade porcine trypsin (Promega, Madison, WI, USA) for 45 min on ice.
After reswelling of the gel spots, the remaining buffer was removed and replaced with
50 mM ammonium bicarbonate. Digestion was carried out at 37°C for 16 h. The
supernatant was removed and the tryptic peptides were extracted first with 25 mM
ammonium bicarbonate and thereafter with 5% formic acid (20 min each). The
combined extracts, as well as the supernatant after digestion, were dried in a
SpeedVac concentrator and dissolved in 10 µL 0.5% formic acid, 5% acetonitrile for
mass spectrometric analysis.
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5.4. Mass spectrometry
5.4.1. Microcapillary liquid chromatography - tandem mass spectrometry (µLC-
MS/MS)
Microcapillary liquid chromatography - tandem mass spectrometry (µLC-MS/MS)
was the technique employed for protein identification from 2D-PAGE spots. Trypsin-
digested protein samples were analyzed using a Finnigan LCQDeca ion trap mass
spectrometer (ThermoFinnigan, San Jose, CA, USA) with Rheos CPS-LC pumps
(Flux Instruments, Basel, Switzerland). Mobile phases were 0.5% formic acid, 5%
acetonitrile (A) and 0.5% formic acid in acetonitrile (B), the flow rate before the
splitter was 300 µL/min (pressure max. 70 bar). Five microliters of the sample were
loaded for 5 min at 100% A using a PAL autosampler with 4°C cooled trays (CTC
Analytics, Zwingen, Switzerland) onto the C18 microcolumn (fused silica, inner
diameter 100 µm, filled with 5 µm Magic C18, column length 5 cm; Spectronex,
Basel, Switzerland). The peptides were eluted with a gradient of 0 - 45% B for
40 min, 45 - 90% B for 10 min and 90% B for 10 min; the column was then
equilibrated with 100% A for 20 min before analyzing the next sample.
Spectra were acquired for 65 min in automated MS/MS mode, consisting of four
sequential scan events: a full MS scan followed by three MS/MS scans on the most
intense, the second most intense and the third most intense ion detected. These four
scan events were repeated throughout the LC run. A dynamic exclusion of ions
measured in any MS/MS scan was automatically carried out for a duration of 30 sec.
For the automatic interpretation of spectra the TurboSEQUEST algorithm (Version
2.0) (Eng, McCormack et al. 1994) was used in screening a human database
downloaded from the NCBI homepage. The chosen parameters were: monoisotopic
160
masses, a mass tolerance of 2.5 Da for the parent ion, tryptic digest. Weighing factors
for the b- and y-ions were set to 1. The number of maximally missed cleavages was
set to 3.
In the case of samples in which a single peptide could be identified, MS/MS spectra
were validated manually employing the following criteria. First, the SEQUEST cross-
correlation score had to be > 2.5. Second, the ratio of the number of experimentally
detected ions that matched to the position of theoretically predicted ones ought to be
more than a defined threshold (e.g., ≥60% for a doubly-charged parent ion). Third, the
MS/MS spectrum ought to be of good quality with fragment ions clearly above
baseline noise. Fourth and last , there had to be continuity to the b or y ion series.
5.4.2. Matrix assisted laser desorption ionization time of flight mass spectrometry
(MALDI-TOF/TOF MS)
For mass spectrometry analysis, HPLC fractions of tryptic digests were redissolved in
50 µl of a solution containing 0.1 % TFA in 50 % ACN and split in two. Each of the
fractions to be analyzed by MALDI-TOF MS received 5 pmol of an internal standard
peptide with the sequence KVPQVSTPTLVEVSR. The fractions were dried in a
Speed Vac concentrator and stored at -20° C until use.
Each lyophilized sample was redissolved in 2 µl of a solution containing 0.1 % TFA
in 50 % ACN. Two µl of a saturated solution of alpha-cyano-4-hydroxycinnamic acid
(matrix) in 0.1 % TFA in 50 % ACN were added to the sample and applied to a
hydrophobic coated MALDI target plate (Perseptive Biosystems, Foster City, CA),
followed by air drying at room temperature.
The target plate was introduced in a Voyager Elite MALDI-TOF mass spectrometer
(Perseptive Biosystems) equipped with a 337 nm nitrogen laser and operating with
161
delayed extraction. The samples were measured in the reflectron mode, positive ions
were accelerated at 25 kV and the laser energy was set to 2400. Forty randomly
chosen spots per sample position were shot with the laser in automated fashion and
the resulting spectra were accumulated. The results of 20 spectra were averaged to
obtain the final spectrum. The MALDI-TOF MS instrument was controlled by the
Voyager Instrument Control Panel Version 5.10.
The data were further processed with the Data Explorer® V4 software (Perseptive
Biosystems).
The resulting MALDI-TOF MS data were used to establish a two-dimensional peptide
map (2D-peptide map) using the in-house developed Spectational software, which
displays the HPLC fractions on the y-axis and the m/z ratios of the measured peptides
on the x-axis. The mass peaks signal intensities are displayed by means of a grayscale,
which is normalized to the peak signal intensity of the internal standard peptide. The
2D-peptide map allows not only the immediate appreciation of the entire mass
spectrometric profile of the chromatographic fractions, but also a comparison between
the same HPLC fraction derived from two different samples. Fractions of interest are
used for subsequent tandem mass spectrometry measurements to identify the proteins
represented by the peptides on the 2D peptide map.
5.5. In vitro biotinylation of surface proteins for 2D peptide mapping
5.5.1. Biotinylation of standard proteins (bovine serum albumin, BSA)
BSA (30 µM) was biotinylated with a hundred fold molar excess of sulfosuccinimidyl
2-(biotinamido)-ethyl-1,3-dithiopropionate (sulfo-NHS-SS-biotin, Pierce, Rockford,
IL) in phosphate buffered saline for 5 min at 4°C. The biotinylation reaction was
162
stopped by adding Tris to a final concentration of 30 mM. Unreacted biotin was
removed from the sample by centrifugation in VIVASPIN concentrators (Vivascience
Sartorius, Goettingen, Germany) with a molecular weight cut off of 10 kDa. After
three rounds of purification, the protein amount was determined using the BioRad
protein assay (BioRad, Hercules, CA).
5.5.2. Biotinylation of cell surface proteins
All solutions employed were cooled to 4°C. Adherent monolayers of HUVEC grown
in two replicate 300 cm2 tissue culture flasks (~2 x 107 cells/flask) were washed once
with phosphate buffered saline (PBS) pH 7.4. Fifteen ml of PBS containing 67 µM
EZ-link sulfo-NHS-SS-biotin (Pierce) were added to each flask and cells were
incubated at room temperature for 5 min. The biotinylation reaction was terminated
by adding Tris-Cl to a final concentration of 670 µM. The cells were washed once
and harvested in 10 ml of a 67 µM oxidized glutathione (Sigma-Aldrich) solution in
PBS by scraping.
The biotinylation of the HEK cell surface proteins was performed as described above,
with the following differences: the HEK cells were biotinylated with 15 ml of a 490
µM sulfo-NHS-SS-biotin solution in PBS. The biotinylation reaction was terminated
by adding Tris-Cl to a final concentration of 4.9 mM. The cells were harvested by
scraping in 10 ml of PBS containing 490 µM oxidized glutathione.
5.5.3. Isolation of biotinylated proteins
The cells were pelleted and lysed for 30 min on ice with 1 mL of lysis buffer
containing 2% w/v Nonidet P-40 substitute (Fluka, Buchs SG, Switzerland), 0.2% w/v
sodium dodecyl sulphate (SDS) (Amersham Biosciences AB, Uppsala, Sweden),
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100 µM oxidized glutathione and protease inhibitors (Complete, EDTA-free from
Roche Diagnostics, Basel, Switzerland). The cell lysate was cleared by centrifugation
(16’100 g, 10 min) followed by the purification of biotinylated proteins on
streptavidin Sepharose high performance (Amersham Biosciences).
Six hundred forty µL of streptavidin Sepharose high performance were washed three
times with wash buffer A (1% w/v Nonidet P-40 substitute and 0.1% w/v SDS in
PBS) before adding the cleared cell lysates. The samples were incubated with
streptavidin Sepharose high performance for two hours on ice. Unbound proteins were
removed by washing three times with wash buffer A, two times with wash buffer B
(0.1% Nonidet P-40 w/v substitute and 0.5 M NaCl in PBS) and once with 50 mM
Tris-HCl, pH 7.5. Captured proteins were eluted from streptavidin Sepharose by
incubation with 0.4 ml of 5% 2-mercaptoethanol (Sigma-Aldrich) in PBS for 30 min
at 30°C. The elution step was repeated three times. The collected eluates were pooled
and the eluted proteins were precipitated by adding 200 µl trichloroacetic acid (TCA)
(Fluka) to a final concentration of 10% and incubating the eluates for 30 min on ice.
The precipitate was pelleted by centrifugation (10’000 g, 5 min), washed with 1 mL
of ethanol-ether (1:1) and air-dried.
The isolation of biotinylated proteins from HEK cells was performed as described
above. One hundred pmol of biotinylated BSA were added as a spike to the
corresponding sample prior to purification of the cell lysate on streptavidin Sepharose.
5.5.4. Digestion of the eluted proteins
The TCA precipitate was dissolved in 200 µl of a 50 mM ammonium hydrogen
carbonate solution and heated to 95°C for 10 min. The solution was cooled on ice and
trypsin (Promega, Madison, WI) was added to a concentration of 60 ng/µL. The
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digestion was carried out for 15 h at 37°C followed by drying the samples in a
SpeedVac concentrator and storing them at -20°C until use.
5.6. Two-dimensional peptide mapping
5.6.1. Reversed-phase HPLC
For chromatographic separation, samples were dissolved in 50 µl of 0.1 % TFA. The
volume of each sample loaded onto the column was standardized in order to obtain
the same area under the curve at 220 nm for all samples (1.3 x 105 mAu x s) within
the time span 7 to 30 min of the gradient.
Chromatography was performed with the Agilent HP1100 system (Agilent, Palo Alto,
CA), which was controlled by the HP-Chemstation chromatography software
(Agilent). The fractionation of the peptides was carried out with a Zorbax-300SB
wide pore column (C18; ID: 500 µm; 0.5 x 150 mm; Agilent), which was kept
constantly at 50° C. Mobile phases used for chromatography were 0.1% trifluoro
acetic acid (TFA) in H2O (buffer A) and 0.1% TFA in acetonitrile (ACN) (buffer B).
Gradient conditions were as follows:
100% buffer A from 0 to 3 min, 80% A/20% B at 4 min, 40% A/60% B at 28 min, 5%
A/95% B at 30 min and 100% A from 32 min to 40 min. The flow rate was 15 µl/min
and the elution of peptides was monitored at 220 nm. In each run, eighty fractions
were collected and dried in a Speed Vac concentrator.
5.6.2. Programming of the Spectational software
Spectational has been programmed with C++ Builder Professional 6.0 (Borland
www.borland.com/cbuilder), build 10.161, and is distributed as a Microsoft Installer
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(MSI) package (http://www.proteios.org/proj/spectational/Spectational.zip; Password:
quantprot). The Spectational MSI was build with Install Shield Express limited
edition (Borland), version 3.03. Input to Spectational is a file containing an ordered
listing of the ASCII formatted spectra to be displayed. These ASCII spectrum files
were produced with the Data Explorer V4 Software (Perseptive Biosystems).
5.6.3. Processing of the MALDI-TOF spectra
After reading the ASCII formatted spectra from disc, these are first baselined and than
normalized. The normalization is done with respect to the internal standard peptide,
which maximum intensity is set to a (deliberate) value of 100. The normalization
tolerates a mass error of maximally 0.3 Dalton. Spectational displays the signal as a
grayscale of the logarithmic values of the normalized intensity. Bin width of the
display is always one pixel, with the value associated to a pixel being the maximum
intensity in the corresponding mass range in the measurement. For detailed reference,
the source code is included in the distributed MSI package.
5.7. In vivo biotinylation of proteins accessible from the bloodstream
5.7.1. Animal experiments
Animal experiments were approved by the Swiss Federal Veterinary Office (license
83/2002 Supplement) and performed in accordance with the Swiss Animal Protection
Ordinance.
Female SV129 mice (RCC, Füllingsdorf, Switzerland) received subcutaneous
injections of ~3-4 x 106 F9 murine teratocarcinoma cells (Berstine, Hooper et al.
1973). The mice were monitored regularly, and tumor volume was measured with a
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caliper. Mice were perfused when tumors reached 400-600 milligrams of weight.
When showing any sign of pain or suffering, when the tumor size exceeded 10% of
the body weight, or in case of a body weight loss >15% animals were euthanatized.
RENCA mice were obtained as described(Ahn, Jung et al. 2001). Briefly, female
Balb/C mice were obtained from Charles River (Charles River, Calco, Italy). The
mice were maintained under specific pathogen-free conditions in approved facilities.
The mice were used when they were 8 weeks old. For in vivo injections, RENCA
cells were harvested by a brief trypsinization. The cells were washed once in serum-
free medium and then resuspended in Hanks’ balanced salt solution (HBSS). Only
single-cell suspensions with greater than 90% viability (tested by trypan blue
exclusion method) were used for injections. To produce kidney tumors, the mice were
anesthetized with ether, RENCA cells (5 x104) in 0.05 ml HBSS were injected into
the renal subcapsule using a 27-gauge needle after a kidney exposure and tumors were
allowed to develop for two weeks prior to in vivo perfusion.
5.7.2. Terminal perfusion of animals and in vivo biotinylation
For perfusion experiments, animals were anaesthetized by a combined subcutaneous
injection of 200-300 mg/kg Ketamin (Vetoquinol, Bern, Switzerland), 20 mg/kg
Xylazin (Streuli, Uznach, Switzerland) and 3 mg/kg Acepromazine (Fatro, Ozzano
Emilia, Italy). The chest of the anaesthetized mouse was opened through a median
sternotomy. The left heart ventricle was punctured with a perfusion needle (25G
butterfly cannula fitted to a barb) and a small cut was made in the right atrium to
allow the outflow of the perfusion solutions. In a first step, blood components were
washed away with pre-warmed PBS (38°C) supplemented with 10% (w/v) Dextran 40
(Amersham Biosciences, Uppsala, Sweden) as plasma expander for 10 minutes.
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Immediately after, a perfusion with 15 ml biotinylation solution was carried out, at a
flow rate of 1.5 ml/min. The perfusion solution contained 1 mg/ml sulfo-NHS-LC-
biotin (Pierce, Rockford, IL, USA) in PBS, pH 7.4, 10% (w/v) Dextran 40 and was
pre-warmed at 38°C. To neutralize unreacted biotinylation reagent, the in vivo
biotinylation was followed by a 10 min washing step with 50 mM Tris in PBS, 10%
(w/v) Dextran 40. Mice in control groups were perfused following exactly the same
protocol, only omitting addition of sulfo-NHS-LC-biotin to the perfusion solution.
In experiments with subcutaneous tumor-bearing mice, the omission of the first step
of perfusion with PBS-dextran significantly improved the rate of biotinylation of the
tumor vasculature. After perfusion, organs and tumors were excised and specimens
were either freshly snap-frozen for preparation of organ homogenates or embedded in
cryoembedding compound (Microm, Walldorf, Germany) and frozen in isopentane in
liquid nitrogen for preparation of cryosections for histochemical analysis.
5.7.3. Preparation of protein extracts from organ homogenates
Biotinylated proteins were purified from different organs and tumor tissue as follows.
Organ specimens were resuspended in 20 µl per mg tissue of a lysis buffer containing
2% SDS, 50 mM Tris, 10 mM EDTA, Complete EDTA-free proteinase inhibitor
cocktail (Roche Diagnostics, Mannheim, Germany) in PBS, pH 7.4 and homogenized
using an Ultra-Turrax T8 disperser (IKA-Werke, Staufen, Germany). Homogenates
were sonicated for 5 min, followed by 5 min incubation at 99°C and 5 min
centrifugation at 16100g. Protein concentration of the total protein extracts was
determined using the BCA Protein Assay Reagent Kit (Pierce).
5.7.4. Biotinylated protein purification
168
SA-Sepharose slurry (100 µl/sample) was washed three times in buffer A (NP40 1%,
SDS 0.1 % in PBS) once in lysis buffer for tissue homogenization (see above),
pelleted and the supernatant removed. Two milligrams of total protein extract from
the different organs were adjusted at 250 µl final volume with lysis buffer and mixed
to the pellet of SA-Sepharose. Capture of biotinylated proteins was allowed to
proceed for 1 h at RT in a revolving mixer. The supernatant was removed and the
resin washed three times with buffer A and two times with buffer B (NP40 0.1%,
NaCl 1M in PBS).
5.7.5. On-resin tryptic digestion of biotinylated proteins
The SA-Sepharose resin with the bound biotinylated proteins was resuspended in 100
µl of ammonium bicarbonate 50 mM and 5 µl of sequencing grade modified porcine
trypsin (Promega, Madison WI) were added. Protease digestion was carried out
overnight at 37°C under constant agitation. The supernatants were collected,
liophylized and resuspended in 20 µl of 0.1% trifluoracetic acid. Peptides were
desalted, purified and concentrated with C18 microcolumns (ZipTip C18, Millipore,
Billerica, MA) following manufacturer’s instructions
5.8. Target validation methods.
5.8.1. Histochemical analysis of biotinylated structures in organ and tumor sections.
Sections from snap-frozen specimens (10 µm) were obtained using a Microm HM
505N cryostat, transferred onto Superfrost Plus glass microslides (Merck, Darmstadt,
Germany), dried for 0.5 - 1 h at 37°C, fixed in chilled acetone for 10 min, dried in air
and finally stored at -80°C. Thawed sections were rehydrated in buffer A (0.01%
169
aprotinin (Sigma-Aldrich, St. Louis, MO, USA), 100 mM NaCl, 50 mM Tris, pH 8.2),
incubated with fetal bovine serum (GIBCO/Invitrogen, Carlsbad, CA) for 30 min to
block unspecific binding sites, washed 2x with buffer C (buffer A supplemented with
2 mM MgCl2), incubated for 45 - 60 min with streptavidin:biotinylated alkaline
phosphatase complex (Biospa, Milano, Italy) diluted 1:200 in buffer C, washed 4
times with buffer C and incubated for 16 - 20 min with a freshly prepared and filtered
solution of 1 mg/ml Fast-Red TR, 0.2 mg/ml Naftolol ASMX Phosphate, 1 mM
Levamisole (all from Sigma), 2 mM MgCl2, 2% (v/v) N,N-Dimethylformamide, 0.1
M Tris, pH 8.2 to develop coloration. The reaction was stopped by immersing glass
slides in water. Sections were counterstained for 3 min in Hematoxylin solution
(Sigma). After washing with water, sections were mounted with Glycergel
(DakoCytomation, Glostrup, Denmark) and analyzed with an Axiovert S100 TV
microscope (Carl Zeiss, Feldbach, Switzerland) using the Axiovision software (Carl
Zeiss).
5.8.2. Western blot analysis
Ten micrograms of lysates from two replicates of normoxic and two replicates of
hypoxic HUVEC were separated by one-dimensional sodium dodecyl sulphate-
polyacrylamide gel electrophoresis (1D SDS-PAGE) on precast 4 to 12% Bis-Tris or
3 to 8 % Tris-acetate gels (Invitrogen, Carlsbad, CA). At the end of the run, proteins
were transferred to nitrocellulose membranes (Millipore, Billerica, MA) with the
Xcell II blot module (Invitrogen) using standard procedures. As a loading control, one
microgram of the same samples was separated by 1D-SDS-PAGE in identical
conditions and the gels were stained by Sypro Ruby (Molecular Probes, Leiden,
Netherlands) following the manufacturers instructions.
170
Replicate, identically obtained membranes were quickly rinsed with H2O before
soaking them twice in methanol. The membranes were dried at room temperature for
15 min and incubated for 1 hour with 1:500 dilutions in 4% defatted milk-containing
PBS of the following antibodies: a mouse monoclonal antibody to beta-catenin (BD
Biosciences, Franklin Lakes, NJ), a rabbit polyclonal antibody to VE-cadherin
(Sigma), a rabbit polyclonal antibody to alpha-catenin (Sigma), a rabbit polyclonal
antibody to fibronectin (Santa Cruz Biotechnology, Santa Cruz, CA) and a mouse
monoclonal antibody to thrombospondin 1 (Neomarkers, Fremont, CA; a kind gift of
Prof. M. Detmar, ETH Zurich). After washing three times for 3 min with PBS, the
appropriate secondary antibody, goat anti-mouse Ig-HRP conjugate (Sigma-Aldrich)
or goat anti-rabbit Ig-HRP conjugate (DAKO, Glostrup, Denmark), was added to the
membranes (1/1,000 dilution). The blots were incubated for 1 hour with the secondary
antibody and washed six times for 3 min with PBS. For detection of immunoreactive
bands the membranes were soaked in chemiluminescent reagent (ECL+plus Western
Blotting Detection System from Amersham Biosciences AB) for 5 min and exposed
to BioMax films (Kodak, Hemel, UK) in an autoradiographic cassette.
For validation of markers identified by in vivo biotinylation procedures, sixty
micrograms of the heart, kidney, liver, skeletal muscle, F9 and RENCA tumor total
protein extract from control, PBS-perfused mice were separated by SDS-PAGE on
replicate 10% Bis-Tris gel with MOPS buffer (Novex/Invitrogen, Carlsbad, CA).
Proteins were stained by Coomassie Brilliant Blue staining, or directly transferred to
nitrocellulose membranes (Schleicher & Schuell, Dassel, Germany) following
standard protocols in a Novex blotting apparatus.
Membranes were pre-blocked in 4% defatted milk, 0.1% Tween 20 in PBS for 1 h at
RT, after which the primary antibody solution was applied.
171
Antibodies anti-Ksp cadherin 16 (Zymed, South San Francisco, CA), anti-calcium
channel L-type DHPR alpha 1 subunit, (Abcam, Cambridge, UK), anti-calcium
channel, voltage gated α2/δ-1 subunit (anti-Cacna2d1) (Chemicon, Temecula, CA)
anti-PTK7 (Abgent, San Diego, CA) and anti-TIAM1 (Santa Cruz Biotechnology,
Santa Cruz, CA) were diluted 1:200 in the same pre-blocking solution described
above and allowed to react for 1 h at RT. After three washings in 0.1% Tween 20 in
PBS, the appropriate secondary antibody conjugated to horseradish peroxidase
(1:500) was applied and allowed to react for 45 min at RT. Membranes were finally
washed again three times with 01% Tween 20 in PBS, twice in PBS and
chemiluminescence detection of immunostained bands was carried out (ECL kit,
Amersham).
5.8.3. Immunofluorescence experiments
Co-staining experiments for the simultaneous detection of biotin and the endothelial
marker CD31 were performed as follows. Sections from snap-frozen specimens
embedded in cryo embedding medium (Microm) and cut as described above, were
incubated with fetal bovine serum (GIBCO/Invitrogen) for 30 min to block unspecific
binding sites, washed twice in PBS, and incubated for 60 min at 37°C with rat anti-
mouse CD31 (Pharmingen, San Diego, CA) in a dilution of 1:100 in 3% (w/v) BSA in
PBS. After washing three times with PBS, sections were incubated for 60 min at 37°C
with goat anti-rat IgG-Cy3 conjugate (Chemicon) diluted 1:50 and 4 µg/ml
streptavidin-Alexa Fluor 488 conjugate (Molecular Probes, Eugene, OR) in a solution
of 3% (w/v) BSA in PBS. After washing in PBS and mounting with 0.1 M Tris-HCl
(pH 9.5)/glycerol (3:7) containing 50 mg/ml n-propyl-gallate (Sigma), sections were
172
analyzed with an Axiomot 2 Mot Plus microscope (Carl Zeiss) using the Axiovision
software.
5.8.4. Indirect immunofluorescence
Mice were fixed by vascular perfusion according to previously described procedures
(Loffing, Loffing-Cueni et al. 2001). The fixative consisted of 3% paraformaldehyde
and 0.05% picric acid dissolved in a 3:2 mixture of 0.1 M cacodylate buffer (pH 7.4,
adjusted to 300 mosm/kg H2O with sucrose) and 10% hydroxyethyl starch in saline
(HAES-sterile, Fresenius, Stans, Switzerland). After 5 minutes of fixation the tissue
of interest was removed, cut into 1-2 mm thick slices, washed for 2 hours in 0.1 M
cacodylate buffer, frozen in liquid propane, and stored at –80° C until use. Cryostat
sections (3-5 µm thick) were preincubated with 10% normal goat serum in PBS,
incubated with mouse monoclonal anti-calcium channel L type DHPR1 alpha subunit
antibody (Abcam), diluted 1 : 200 in PBS or with mouse monoclonal anti-Ksp
cadherin (Zymed), diluted 1 : 200 in PBS over night at 4°C in a humidified chamber.
After repeated washings with PBS, binding sites of the primary antibodies were
revealed with FITC-conjugated goat-anti-mouse IgG (Jackson ImmunoResearch
Laboratories, West Grove, PA), diluted 1/40 in PBS/BSA 1%, to which per 1 ml of
the working dilution 2 µg of 4,6-diamidino-2-phenylindole dihydrochloride (DAPI,
Boehringer, Mannheim, Germany) was added for nuclear staining. Subsequently,
sections were washed with PBS and coverslips were mounted with DAKO-Glycergel
(Dakopatts, Glostrup, Denmark), to which 2.5% 1,4-diazabicyclo-(2,2,2)-octane
(DABCO, Sigma) was added to retard fading. For control of unspecific antibody
binding, the primary antibodies were omitted.
173
6. Acknowledgements
I would like to express my gratitude to Prof. Dario Neri, who allowed me in his
Group, introduced me into the field of vascular targeting and constantly supported my
activity with his invaluable enthusiasm, determination and clear-mindedness.
He is the source of many of the ideas that allowed the evolution of proteomic science
in the Group. Our frequent discussions and “brain-storming” meetings have
represented for me a fundamental reference point throughout the whole period I spent
at ETH. An outstanding scientist and a perfect gentleman at the same time, with his
example Dario has been teaching me a lot during the last 5 years. I feel profoundly
indebted to him.
I would also like to thank Maja A. Bumke, Simone B. Scheurer, Jascha-Nikolai
Rybak, Christoph Rösli and Anna Ettorre who, with their commitment and their hard
work, have contributed during these years to the successful activity of our Proteomic
Unit. Many thanks also to the many Diploma students and Semesterarbeit students
who have worked under my supervision.
Special thanks to all other members of the Neri Group, past and present, for the many
stimulating discussions and their friendly attitude, which made of working here a
particularly rewarding experience.
Thanks to all the Colleagues of the Institute of Pharmaceutical Sciences for the nice
interactions and exchanges we had, and to the members of the Functional Genomics
Center Zurich (coordinated by Ralph Schlapbach) for granting access to the
instrumentation of the Center and for technical support to the transcriptomic and
proteomic experiments described in this thesis.
174
Finally, I would like to say thanks to Dominique, to Alessandra, to my family and
friends for the daily gift of their precious love.
175
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8. Appendix A
Supplementary table 1
Proteins identified in extracts of HUVEC grown in hypoxic conditions by 2D peptide mapping SwissProt Fraction
ID Common name Accession Peptide H1a H2a
1 PECAM-1 P16284 (R) ANHASSVPR 20 20 (R) IYDSGTYK 21 (K) NSNDPAVFK 22 (K) DTETVYSEVR 22 22 (K) KDTETVYSEVR 22 (K) EAIQGGIVR 23 22,23,24 (K) EDTIVSQTQDFTK 24,30,31 24,29,30 (R) IISGIHMQTSESTK 24 24 (K) MLSEVLR 26 (K) APIHFTIEK 26 26 (K) STESYFIPEVR 28,29 (K) TTAEYQVLVEGVPSPR 28,29 28,29 (R) ISYDAQFEVIK 28 28 (K) VIAPVDEVQISILSSK 33,34 33,34 (R) EPVFVGGVPESLLTPR 34 34
2 Cell surface glycoprotein MUC18 [Precursor] P43121 (R) QGQGQSEPGEYEQR 20 20 (R) GATLALTQVTPQDER 25 25 (R) EAEEETTNDNGVLVLEPAR 26 26 (K) VWLEVEPVGMLK 35
3 Vascular endothelial-cadherin [Precursor] P33151 (R) YMSPPAGNR 21 21 (R) PSLYAQVQKPPR 23 23 (R) EYEIVVEAR 24 (K) EYFAIDNSGR 24 24 (K) YTFVVPEDTR 26 26 (K) MLAELYGSDPR 26 26 (K) DTGENLETPSSFTIK 26 26 (K) VHDVNDNWPVFTHR 27 27 (K) VDAETGDVFAIER 27 27 (K) KPLIGTVLAMDPDAAR 32 32 (K) VHFLPVVISDNGMPSR 32 32
4 Laminin beta-1 chain [Precursor] P07942 (K) AAQNSGEAEYIEK 21 (K) ISGVIGPYR 24 (K) ELDSLQTEAESLDNTVK 28 (R) IPSWTGAGFVR 29 29 (R) VESLSQVEVILQHSAADIAR 37 37
5 Semaphorin 6B [Precursor] Q9H3T3 (R) APEQPPAPGEPTPDGR 21 6 T-cell surface glycoprotein E2 [Precursor] P14209 (K) ENAEQGEVDMESHR 21 7 CD 109 Q8TDJ3 (R) GQLVAVGK 22
(R) ADGNQLTLEER 22 (K) DYIDGVVYDNAEYAER 24 (R) NSLGGFASTQDTTVALK 26 (K) TLSFSFPPNTVTGSER 30,31 30,31 (R) FLVTAPGIIR 31 31,32 (K) EALNMLTWR 31 31 (K) ALSEFAALMNTER 32 32
229
(K) SYSQSILLDLTDNR 32 32 (K) TLTLPSLPLNSADEIYELR 38 37,38
8 Intercellular adhesion molecule-2 [Precursor] P13598 (R) MGTYGVR 22 (R) RLPQAFRP 25
9 Fibronectin [Precursor] P02751 (R) PQAPITGYR 22 (K) TYHVGEQWQK 23 (K) HYQINQQWER 23 (R) GDSPASSKPISINYR 23 23 (R) FTNIGPDTMR 24 24 (K) WLPSSSPVTGYR 26 26 (R) ITYGETGGNSPVQEFTVPGSK 26 (R) EESPLLIGQQSTVSDVPR 27 (R) VDVIPVNLPGEHGQR 28 (K) GLAFTDVDVDSIK 30 29 (K) VTIMWTPPESAVTGYR 31 10 Perlecan P98160 (R) HQTHGSLLR 22 (R) IAHVELADAGQYR 24 (K) SPAYTLVWTR 29 (R) YELGSGLAVLR 30 (R) HPTPLALGHFHTVTLLR 32 32 11 Keratin, type I cytoskeletal 10 or P13645 (R) LENEIQTYR 22 Keratin 10 Q8N175 12 Hedgehog-interacting protein Q96QV1 (R) VVEYTVSR 22 (R) LYGSYVFGDR 27 27 (R) VFLEVAELHR 29 29 13 Integrin beta-1 [Precursor] or P05556 (K) SAVTTVVNPK 23 23,24 Hypothetical protein (Q8WUM6) Q8WUM (K) WDTGENPIYK 24 24 (K)SGEPQTFTLK 24 (K) LKPEDITQIQPQQLVLR 31 31 (K) SLGTDLMNEMR 29 29 14 Basigin P35613 (R) FFVSSSQGR 23 23 15 PEG8/IGF2AS protein Q9P2W0 (K) GPQSAPPSPPAGRR 23 16 Beta-catenin P35222 (R) TSMGGTQQQFVEGVR 24 23 (R) NEGVATYAAAVLFR 35 35 17 Endoglin [Precursor] P17813 (R) TLEWRPR 24 24 (K) TQILEWAAER 28 28,29 (K) LPDTPQGLLGEAR 28 28 (R) GPITSAAELNDPQSILLR 31,32,33 31,32 18 Integrin alpha-2 [Precursor] P17301 (R) SHLQYFGR 24 (K) TQVGLIQYANNPR 25 25 (K) QAFDQILQDR 27 27 (K) IPLLYDAEIHLTR 34 (R) FGIAVLGYLNR 36 19 Keratin, type II cytoskeletal 1 P04264 (K) YEELQITAGR 24 (K) QISNLQQSISDAEQR 24 (K) SLNNQFASFIDK 30 (R) THNLEPYFESFINNLR 38 20 Nidogen [Precursor] or P14543 (R) ASLHGGEPTTIIR 24 Similar to nidogen Q86XD7 (R) NIFWTDSNLDR 29 29 (R) VLFETDLVNPR 30 (K) MVYWTDITEPSIGR 31
21 Dihydrolipoamide succinyltransferase component P36957 (K) TPAFAESVTEGDVR 24
230
of 2-oxoglutarate dehydrogenase complex, mitochondrial [Precursor] 22 Prostaglandin F2 receptor negative regulator Q9P2B2 (R) MYQTQVSDAGLYR 24 [Precursor]
Sodium channel protein type I alpha subunit or 23 P35498 (K) ILDEIKPLDDLNNK 24
Voltage-gated sodium channel alpha subunit Q9P2Q6 [Fragment] 24 Laminin alpha-4 chain [Precursor] Q16363 (R) LAALSIEEGK 25 (K) QYNDGLSHFVISSVSPTR 30 (R) AHLPLDINFR 31 (R) EPVFVGGVPESLLTPR 34 34 (K) RPELTETADQFILYLGSK 37 25 Alpha-1 catenin or P35221 (K) LLSNTVMPR 25 Alpha2(E)-catenin Q2795 (K) QIIVDPLSFSEER 33 32 26 Integrin alpha-V [Precursor] P06756 (K) SHQWFGASVR 25 (K) AGTQLLAGLR 27 27 Integrin alpha-5 [Precursor] P08648 (R) VTAPPEAEYSGLVR 25 25 (R) QATLTQTLLIQNGAR 28 (K) AGASLWGGLR 29 28,29 (K) SLQWFGATVR 31 30,31 28 Keratin, type I cytoskeletal 9 P35527 (K) TLLDIDNTR 26 26 29 Thrombospondin 1 [Precursor] P07996 (K) FQDLVDAVR 26 (K) GGVNDNFQGVLQNVR 28 28 (R) FVFGTTPEDILR 33,34 33 (K) MENAELDVPIQSVFTR 33 33 (K) GFLLLASLR 37 (R) IPESGGDNSVFDIFELTGAAR 39 39
Tubulin beta-1 chain or 30 P07437 (R) FPGQLNADLR 26 Tubulin beta-1 chain or Q9H4B7 (K) LAVNMVPFPR 31 Tubulin beta-2 chain or P05217 (R) AILVDLEPGTMDSVR 31 Tubulin beta-4 chain or Q13509 Similar to tubulin, beta 4 or Q9BUF5 Tubulin beta-5 chain or P05218 Tubuin beta-5 chain or P04350 Tubulin beta-4q chain or Q99867 Beta-tubulin 4q or Q8WZ78
Hypothetical protein or Q969E5 Tubulin, beta polypeptide or Q9BVA1 Probable beta tubulin (Fragment) or O43209 Beta tubulin or Q13885 Tubulin, beta 4 Q8WUL7
31 Protein-glutamine gamma-glutamyltransferase 2 P21980 (R) DLYLENPEIK 27 26
(R) VVTNYNSAHDQNSNLLIEYFR 33 33 (R) ALLVEPVINSYLLAER 38 37,38
32 Protein-tyrosine phosphatase beta [Precursor] or P23467 (R) TVVLQTDPLPPAR 26
Similar to protein tyrosine phosphatase, Q86VA4 (K) EETQYVMDDTGLVPGR 26 receptor type, B (R) VLAPWITETHFK 32 (K) EFTFTDLVPGR 33 33
33 Angiotensin-converting enzyme, somatic isoform P12821 (K) QDGFTDTGAYWR 26
34 ATP synthase alpha chain, mitochondrial [Precursor] P25705 (K) TGTAEMSSILEER 26
231
35 Caveolin-1 Q03135 (K) YVDSEGHLYTVPIR 26 36 Laminin gamma-1 chain [Precursor] P11047 (K) AFDITYVR 27 (R) QDIAVISDSYFPR 30 29,30 (R) PALTPFEFQK 30 (R) LSAEDLVLEGAGLR 32 32 37 Fibronectin [Precursor] or P02751 (K) IYLYTLNDNAR 27 27 Fibronectin (fragment) or Q96KP9 (R) PAQGVVTTLENVSPPR 27 27 Fibronectin (fragment) or Q9UQS6 Fibronectin Q96KP8 38 Elongation factor 1-alpha 1 P04720 (K) YYVTIIDAPGHR 27 27 40 CD166 antigen [Precursor] Q13740 (K) ALFLETEQLK 29 29 (K) YEKPDGSPVFIAFR 31 31 41 Tubulin alpha-1 chain or P05209 (R) FDGALNVDLTEFQTNLVPYPR 37 Tubulin alpha-2 chain or Q13748 (R) QLFHPEQLITGK 29 29 Tubulin alpha-6 chain or Q9BQE3 Tubulin alpha-4 chain or P05215 Tubulin alpha-8 chain or Q9NY65 Similar to tubulin alpha 2 Q8WU19 42 Actin, aortic smooth muscle or P03996 (K) SYELPDGQVITIGNER 29 29 Actin, cytoplasmic or P02570 Actin, alpha cardiac or P04270 Actin, cytoplasmic2 or P02571 Actin, gamma-enteric smooth muscle or P12718 Actin, alpha skeletal muscle or P02568 FKSG Q9BYX7
43 PlexinA1 or A2 or B1 or 4 or 6 or B1/SEP receptor Q9Y4D7 (R) KGFAELQTDMTDLTKELNR 29
or Plexin D1 (Blast search from Hypothetical prot.)
44 Similar to calcium binding protein 1 Q8N6H5 (R) PREALPAAASRPSPSSPLPPAR 29 45 Hypothetical protein Q86WZ0 (K) PVSFKFLGSSSPLTGDTSLAVK 29
46 Dihydrolipoamide succinyltransferase component P36957 (K) VEGGTPLFTLR 30
of 2-oxoglutarate dehydrogenase complex, mitochondrial [Precursor] or Alpha-ketoglutarate dehydrogenase complex Q16187 dihydrolipoyl succinyltransferase or
Human full-length cDNA 5-PRIME end of clone Q86TQ8
CS0DB006YE12 of neuroblastoma of Homo sapiens
[Fragment] 47 Myeloblast KIAA0230 [Fragment] Q92626 (R) IPSGAFEDLENLK 30 (R) AEGNPKPEIIWLR 31
48 Dihydrolipoamide acetyltransferase component P10515 (K) VPLPSLSPTMQAGTIAR 30
of pyruvate dehydrogenase complex, mitochondrial
[Precursor] or
RNA-binding RING-H2 protein-ubiquitin ligase Q86Y15
[Fragment] 49 Tubulin alpha-1 chain or P05209 (K) TIGGGDDSFNTFFSETGAGK 30 30 Tubulin alpha-2 chain or Q13748 Tubulin alpha-6 chain or Q9BQE3
232
Similar to tubulin alpha 2 or Q8WU19 Hypothetical protein Q8N350
50 Complement component C1q receptor [Precursor] Q9NPY3 (K) FWIGLQR 31
51 Similar to T54 protein Q9BQA8 (R) LADSGDGAGPSPEEK 31 52 Agrin [Precursor] [Fragment] O00468 (R) TFVEYLNAVTESEK 31 53 ATP-dependent RNA helicase Q8TDD1 (K) LLVEFARAGLTEPVLIR 31 54 Tubulin beta-1 chain or P07437 (R) YLTVAAVFR 32 Tubulin beta-2 chain or P05217 Tubulin beta-5 chain or P05218 Tubuin beta-5 chain or P04350 Hypothetical protein Q969E5 55 Peroxiredoxin 6 P30041 (K) LPFPIIDDR 32 56 Tubulin alpha-1 chain or P05209 (K) EIIDLVLDR 32 Tubulin alpha-6 chain or Q9BQE3 (R) AVFVDLEPTVIDEVR 34 Hypothetical protein Q8N532 57 Endothelial protein C receptor [Precursor] Q9UNN8 (R) LHMLQISYFR 32 32 58 Axonemal dynein heavy chain 8 Q96JB1 (K) LVQLYETSLVR 32 59 60S ribosomal protein L18 Q07020 (K) ILTFDQLALDSPK 32 60 Filamin B O75369 (K) SPFTVGVAAPLDLSK 32 61 Ubiquitin carboxyl-terminal hydrolase 37 Q86T82 (K) RSLGFLPQPVPLSVK 32 62 60S ribosomal protein L12 P30050 (R) QAQIEVVPSASALIIK 32
63 Tyrosine-protein kinase receptor Tie-1 [Precursor] P35590 (R) GSDAWGPPLLLEK 33
64 78 kDa glucose-regulated protein [Precursor] P11021 (K) TFAPEEISAMVLTK 34 34
65 Dihydrolipoamide branched chain transacylase P11182 (K) DMTVPILVSKPPVFTGK 34 34
66 Reticulocalbin 3 [Precursor] Q96D15 (R) PSVLLLLLLLRHGAQGK 34 34
67 Thioredoxin domain containing protein 5 [Precursor] Q8NBS9 (R) GYPTLLLFR 36
68 Vascular endothelial growth factor receptor 3 P35916 (R) VLFARFSKTEGGAR 37 [Precursor] 69 Tubulin beta-1 chain or P07437 (R) LHFFMPGFAPLTSR 38 38 Tubulin beta-2 chain or P05217 (K) GHYTEGAELVDSVLDVVR 38 38 Tubulin beta-5 chain or P05218 Hypothetical protein or Q969E5 Tubulin, beta polypeptide or Q9BVA1 Beta-tubulin 4q or Q8WZ78 Similar to tubulin, beta 4 or Q9BUF5 Probable beta tubulin (Fragment) O43209 70 Protein-tyrosine phosphatase beta [Precursor] P23467 (R) AQVDPLVQSFSFQNLLQGR 39 39 71 Monocarboxylate transporter 1 P53985 (K) ESKEEETSIDVAGKPNEVTK 22
a = H1 and H2 indicate two replicate samples of HUVEC grown in hypoxic conditions
233
9. Appendix B
Supplementary Table 2
Complete list of proteins identified in mouse organs and tumors
Protein name SwissProtaccession
Kidney 0 day neonate head cDNA, RIKEN full-length enriched library Q9D2K8
Q9D1H9 1200011D11Rik protein Q9DBX34F2 cell-surface antigen heavy chain P10852
Q80V42 Q9CX32
A Actin, aortic smooth muscle P03996 Actin, cytoplasmic 1 P02570
P51881 Alcohol dehydrogenase [NADP+] Q9JII6
P09242 Alpha enolase P17182 Alpha-1-antitrypsin 1-1 precursor P07758 Alpha-1-antitrypsin 1-4 precursor Q00897 Alpha-2-macroglobulin precursor Q61838
P97449 Antithrombin-III precursor P32261
Q00623 Q9EQT9
Argininosuccinate synthase P16460 Q91XE4 Q99MQ4
ATP synthase alpha chain Q03265 Q8VDN2
B Q9D1W8
Basement membrane-specific heparan sulfate proteoglycan core protein precursor
Q05793
Basigin precursor P18572 P28653
C O88338 P12658 Q8CI85
Q9WVT6P21181
1110007F23Rik protein
5730456K23Rik protein (Fragment) 9430025F20Rik protein
ADP,ATP carrier protein, fibroblast isoform
Alkaline phosphatase, tissue-nonspecific isozyme precursor
Aminopeptidase N
Apolipoprotein A-I precursor ARG1
Aspartoacylase 2 Asporin precursor
ATPase, Na+K+ transporting, alpha 1 polypeptide
B830026H24Rik protein
Biglycan precursor
Cadherin-16 precursor Calbindin Carbonic anhydrase XII precursor Carbonic anhydrase XIV precursor Cell division control protein 42 homolog
234
Collagen alpha 1(I) chain precursor
Collagen alpha 1(XVIII) chain precursor
Collagen type XIV precursor
Cytochrome c oxidase polypeptide II Cytochrome P450
DNA segment, Chr 10, Johns Hopkins University 81 expressed
EGP314 precursor
Epithelial-cadherin precursor
Extracellular superoxide dismutase [Cu-Zn] precursor
Gamma-glutamyltranspeptidase 1 precursor Glutamate carboxypeptidase II
Glutamyl aminopeptidase
Hydroxyacid oxidase 3
Hypothetical type I phosphodiesterase / nucleotide pyrophosphatase containing protein Heat-responsive protein 12
Ig gamma-2A chain C region secreted form Ig heavy chain V region AC38 205.12 Ig kappa chain C region Integrin alpha-3 precursor
P11087 Collagen alpha 1(VI) chain precursor Q04857
P39061 Collagen alpha 2(VI) chain precursor Q02788
Q80X19 Complement C3 precursor P01027 Complement C4 precursor P01029
P00405 O35728
D Decorin precursor P28654 Dipeptidyl peptidase IV P28843
Q9D172 E Ectonucleotide pyrophosphatase/phosphodiesterase 1 P06802
Q61512 Elongation factor 1-alpha 1 P10126 Elongation factor 2 P58252
P09803 Esterase 10 Q9CWI4
O09164 F Fatty acid transport protein 2 O88560 Fibrinogen A alpha polypeptide Q99K47 Fibrinogen, B beta polypeptide Q8K0E8 Fibronectin precursor P11276 Fructose-bisphosphate aldolase B Q91Y97 Fumarylacetoacetase P35505 G
Q60928 O35409
Glutamate dehydrogenase, mitochondrial precursor P26443 P16406
Glutathione peroxidase P11352 Glutathione S-transferase theta 2 Q61133 Glyceraldehyde 3-phosphate dehydrogenase P16858 H Histone H2A.1 P22752 Histone H2B F P10853
Q9NYQ2Hypothetical protein Q8K0L3
Q8BGN3
P52760 Hemoglobin alpha, adult chain 1 Q9CY10 I
P01864 P06330 P01837 Q62470
235
Integrin alpha-6 precursor
Iron-responsive element binding protein 1
Kidney-specific membrane protein NX-17 Kidney-specific transport protein Kynurenine aminotransferase II
Lipoprotein receptor-related protein
LOC240505 protein (Fragment) Low affinity sodium-dependent glucose cotransporter
MECA32
Methylmalonate-semialdehyde dehydrogenase
Peroxiredoxin 5, mitochondrial precursor Peroxisomal bifunctional enzyme
Phosphoenolpyruvate carboxykinase, cytosolic [GTP]
Prolargin precursor Prominin 1 precursor
Quinone oxidoreductase
Q61739 Integrin beta-1 precursor P09055
P28271 Isocitrate dehydrogenase [NADP] cytoplasmic O88844 Isocitrate dehydrogenase 2 Q8C2R9 K Keratin 6 alpha Q9Z332 Keratin, type II cytoskeletal 1 P04104 Ketohexokinase P97328
Q9ESG4 Q61185
Q9WVM8L Laminin alpha-5 chain precursor Q61001 Laminin gamma-1 chain precursor P02468
Q91ZX7 L-lactate dehydrogenase A chain P06151 L-lactate dehydrogenase B chain P16125
Q80UW5Q923I7
Lumican precursor P51885 Lutheran blood group Q99K86 M Malate dehydrogenase, mitochondrial precursor P08249
Q91VC4 Methylcrotonyl-CoA carboxylase alpha chain Q99MR8
Q9EQ20 Microsomal dipeptidase precursor P31428 Murinoglobulin 1 precursor P28665 N NADH-ubiquinone oxidoreductase chain 4 P03911 NADH-ubiquinone oxidoreductase chain 5 P03921 Nidogen precursor P10493 Nidogen-2 precursor O88322 P Pancreas sodium bicarbonate cotransporter O88343 Peptidyl-prolyl cis-trans isomerase A P17742
P99029 Q9DBM2
Phosphatidylethanolamine-binding protein P70296 Q9Z2V4
Phosphoglycerate kinase 1 P09411 Plasma glutathione peroxidase precursor P46412 Platelet glycoprotein IV Q08857
Q9JK53 O54990
Propionyl-coenzyme A carboxylase, alpha polypeptide Q80VU5 Pyruvate carboxylase Q05920 Q
P47199
236
S Serine proteinase inhibitor A3K precursor P07759 Serotransferrin precursor Q921I1 Serum albumin precursor P07724 Short chain 3-hydroxyacyl-CoA dehydrogenase, mitochondrial precursor Q61425
Q8QZT1 Similar to acetyl-coenzyme A acyltransferase 2 Q8JZR8 Similar to aldehyde dehydrogenase 4 family, member A1 Q8CHT0
Q91X28 Similar to DJ-1 protein Q99LX0 Similar to DKFZP586B1621 protein Q8VC30 Similar to fibrinogen, gamma polypeptide Q8VCM7
Q80ZI8 Similar to mitochondrial aconitase Q99KI0
Q8R0N5 Q91WR5
Similar to tubulointerstitial nephritis antigen Q91XG7 Q9WV27
Sodium/potassium-transporting ATPase beta-1 chain P14094 Q04646
Solute carrier family 25, member 13 Q9QXX4Q8BUG0Q9WUM5
T Q8K1T0
Transthyretin precursor P07309 Triosephosphate isomerase P17751 Tubulin alpha-1 chain P02551 Tubulin beta-2 chain P05217 Type VI collagen alpha 3 subunit O88493 Type XV collagen O35206 U
Q9CZ13 UDP-glucuronosyltransferase 1-1 precursor, microsomal Q63886 V
P50516 O35488
Voltage-dependent anion-selective channel protein 1 Q60932 Voltage-dependent anion-selective channel protein 2 Q60930
Liver 0610012J07Rik protein Q9DCP1 10-formyltetrahydrofolate dehydrogenase Q8R0Y6 1200011D11Rik protein Q9DBX31200014D15Rik protein Q9DBT9
Similar to acetyl-Co A acetyltransferase 1
Similar to carbonyl reductase 1
Similar to integrin alpha 6 (Fragment)
Similar to quinone reductase-like protein (Fragment) Similar to RIKEN cDNA 9430025F20 gene
Sodium/potassium-transporting ATPase alpha-4 chain
Sodium/potassium-transporting ATPase gamma chain
Solute carrier family 4 Succinyl-CoA ligase
Transmembrane protease, serine 3
Ubiquinol-cytochrome C reductase complex core protein I
Vacuolar ATP synthase catalytic subunit A, ubiquitous isoform Very-long-chain acyl-CoA synthetase
Proteins marked in red were identified only in kidney extracts
237
1300018L09Rik protein Q9DBB818 days embryo cDNA, RIKEN full-length enriched library Q9D160 2-hydroxyphytanoyl-CoA lyase Q9QXE03,2-trans-enoyl-CoA isomerase, mitochondrial precursor P42125 3-hydroxy-3-methylglutaryl-coenzyme A lyase Q8QZS6 3-ketoacyl-CoA thiolase Q8BWT14-hydroxyphenylpyruvate dioxygenase P49429 4-trimethylaminobutyraldehyde dehydrogenase Q9JLJ2 60 kDa heat shock protein, mitochondrial precursor P19226 A Actin, aortic smooth muscle P03996 Actin, cytoplasmic 1 P02570 Acyl-CoA-binding protein P31786 Acyl-coenzyme A oxidase 1, peroxisomal Q9R0H0 Adenosylhomocysteinase P50247 Alcohol dehydrogenase [NADP+] Q9JII6 Alcohol dehydrogenase A chain P00329 Alcohol dehydrogenase class III P28474 Aldehyde dehydrogenase 1A1 P24549 Aldehyde dehydrogenase, mitochondrial precursor P47738 Aldo-keto reductase family 1 member C13 Q8VC28 Alpha enolase P17182 Amine oxidase Q8C0B2 Amylo-1 Q8CE68 Apolipoprotein C-III precursor P33622 Apolipoprotein E precursor P08226 Arginase 1 Q61176 Argininosuccinate synthase P16460 Asialoglycoprotein receptor 1 (Hepatic lectin 1) P34927 Aspartate aminotransferase, cytoplasmic P05201 Aspartate aminotransferase, mitochondrial precursor P05202 ATP synthase alpha chain, mitochondrial precursor Q03265 ATP-binding cassette, sub-family D, member 3 P55096 ATP-citrate synthase Q91V92 B Basigin precursor P18572 Beta-alanine oxoglutarate aminotransferase Q8BZA3 Betaine-homocysteine S-methyltransferase O35490 C Calcium-binding mitochondrial carrier protein Aralar2 Q9QXX4Carbonic anhydrase III P16015 Carboxylesterase 3 Q8VCT4 Catalase P24270 Cathepsin B precursor P10605 Cis-retinol/androgen dehydrogenase type 3 Q8K5C8 Cystathionine gamma-lyase Q8VCN5D Delta-aminolevulinic acid dehydratase P10518 Dipeptidyl peptidase IV P28843 E
238
Ectonucleotide pyrophosphatase/phosphodiesterase 1 P06802 Electron transfer flavoprotein alpha-subunit Q99LC5 Elongation factor 1-alpha 1 P10126 Elongation factor 2 P58252 Endoglin precursor Q63961 Endoplasmin precursor P08113 Esterase 10 Q9CWI4 Estradiol 17 beta-dehydrogenase 5 P70694 F Fatty acid synthase Q9EQR0 Fatty acid transport protein 2 O88560 Fatty acid-binding protein, liver P12710 Fibrinogen, B beta polypeptide Q8K0E8 Fibronectin precursor P11276 Fructose-1,6-bisphosphatase Q9QXD6Fructose-bisphosphate aldolase B Q91Y97 Fumarylacetoacetase P35505 G Gamma-glutamyltransferase 5 precursor Q9Z2A9 Gamma-glutamyltransferase-like activity 1 Q8C7B4 Glutamate dehydrogenase, mitochondrial precursor P26443 Glutaryl-CoA dehydrogenase, mitochondrial precursor Q60759 Glutathione peroxidase P11352 Glutathione S-transferase Mu 1 P10649 Glutathione S-transferase theta 2 Q61133 Glutathione S-transferase Yc P30115 Glutathione transferase omega 1 O09131 Glyceraldehyde 3-phosphate dehydrogenase P16858 Glycogen phosphorylase, liver form Q9ET01 H H-2 class I histocompatibility antigen, D-B alpha chain precursor P01899 H-2 class I histocompatibility antigen, K-B alpha chain precursor P01901 Hemoglobin alpha, adult chain 1 Q9CY10 Hemoglobin beta-1 chain P02088 Histidine ammonia-lyase P35492 Histone H2A.1 P22752 Histone H2B F P10853 Homogentisate 1,2-dioxygenase O09173 Hydroxymethylglutaryl-CoA lyase, mitochondrial precursor P38060 Hydroxymethylglutaryl-CoA synthase P54869 Hypothetical protein Q91W19 Hypothetical protein Q8CC86 Hypothetical protein (Activated leukocyte cell adhesion molecule) Q8R2T0 Hypothetical protein (Fragment) Q99KD0 Hypothetical protein (Staphylococcal nuclease domain containing 1) Q922L5 I Integrin beta-1 precursor P09055 Isocitrate dehydrogenase [NADP] cytoplasmic O88844 K Keratin, type II cytoskeletal 1 P04104
239
Ketohexokinase P97328 L L-lactate dehydrogenase A chain P06151 M Macrophage mannose receptor precursor Q61830 Maleylacetoacetate isomerase Q9WVL0Methylcrotonyl-CoA carboxylase alpha chain Q99MR8N N system amino acids transporter NAT-1 Q9JLL8 NADH-ubiquinone oxidoreductase chain 4 P03911 NADH-ubiquinone oxidoreductase chain 5 P03921 NADP-dependent malic enzyme P06801 NDRG2 protein Q9QYG0Neural-cadherin precursor (N-cadherin) P15116 O Ornithine carbamoyltransferase, mitochondrial precursor P11725 P Peptidyl-prolyl cis-trans isomerase A P17742 Peroxiredoxin 6 O08709 Peroxisomal multifunctional enzyme type 2 P51660 Phosphatidylethanolamine-binding protein P70296 Phosphoglucomutase Q9D0F9 Phosphoglycerate kinase 1 P09411 Platelet glycoprotein IV Q08857 Probable urocanate hydratase Q8VC12 Propionyl-coenzyme A carboxylase, alpha polypeptide Q80VU5 Pyruvate carboxylase Q05920 R RIKEN cDNA 1500002K10 gene Q91VS7 S Scavenger receptor class B member 1 Q61009 SEC14-like protein 2 Q99J08 Selenium-binding protein 1 P17563 Senescence marker protein-30 Q64374 Serum albumin precursor P07724 Short chain 3-hydroxyacyl-CoA dehydrogenase Q61425 Similar to acetyl-CoA acyltransferase, 3-oxo acyl-CoA thiolase A, peroxisomal
Q8VCH0
Similar to acetyl-coenzyme A acyltransferase 2 Q8JZR8 Similar to aldehyde dehydrogenase 4 family, member A1 (Fragment) Q8CHT0 Similar to aldehyde dehyrdogenase 8 family, member A1 Q8BH00 Similar to CG6385 gene product Q99LB7 Similar to DJ-1 protein Q99LX0 Similar to DKFZP586B1621 protein Q8VC30 Similar to fibrinogen, gamma polypeptide Q8VCM7Similar to glutamic-pyruvate transaminase Q8QZR5 Similar to glycine N-methyltransferase Q91WN7Similar to methionine adenosyltransferase I, alpha Q91X83 Similar to mitochondrial aconitase Q99KI0 Sodium/potassium-transporting ATPase beta-1 chain P14094
240
Sodium/potassium-transporting ATPase beta-3 chain P97370 Soluble epoxide hydrolase P34914 Solute carrier family 21 member 10 Q9JJL3 Solute carrier family 25, member 13 Q9QXX4Sorbitol dehydrogenase Q64442 Sulfotransferase-related protein O35403 Superoxide dismutase [Cu-Zn] P08228 T T-cell surface glycoprotein CD1d1 precursor P11609 T-complex protein 1 Q8CAY6Thioether S-methyltransferase P40936 Trifunctional enzyme alpha subunit Q8BMS1Triosephosphate isomerase P17751 U UDP-glucuronosyltransferase 1-1 precursor, microsomal Q63886 V Vitamin D-binding protein precursor P21614 W Weakly similar to carbamoyl-phosphate synthase (Fragment) Q8C196 Proteins marked in blue were identified only in liver extracts
Muscle A
P02568 Alpha-1-antitrypsin 1-3 precursor Q00896 Alpha-1-antitrypsin 1-4 precursor Q00897 Alpha-2-macroglobulin precursor Q61838 Apolipoprotein A-IV precursor P06728 B Basement membrane-specific heparan sulfate proteoglycan core protein precursor
Q05793
P21550 Q91Z83
C Q9WTR5Q8R429
Collagen alpha 1(VI) chain precursor Q04857 Q60847
Collagen alpha 2(VI) chain precursor Q02788 Complement C3 precursor P01027
Q62257 Creatine kinase, M chain P07310 D Decorin precursor P28654
O08532
Actin, alpha skeletal muscle
Beta enolase Beta myosin heavy chain
Cadherin-13 precursor Calcium-transporting ATPase
Collagen alpha 1(XII) chain precursor
Contrapsin precursor
Dihydropyridine-sensitive L-type, calcium channel alpha-2/delta subunits precursor
241
E Q8R2G4
F P50608
Fructose-bisphosphate aldolase A P05064 Fructose-bisphosphate aldolase B Q91Y97 G
P13020 Glyceraldehyde 3-phosphate dehydrogenase P16858
Q60935 H Heat shock protein HSP 90-alpha P07901 I
P01756 Inter-alpha-trypsin inhibitor heavy chain H3 precursor Q61704 L Laminin alpha-2 chain precursor Q60675 Laminin beta-1 chain precursor P02469 Laminin gamma-1 chain precursor P02468 L-lactate dehydrogenase A chain P06151 Lumican precursor P51885 M
O70423 Methylcrotonyl-CoA carboxylase alpha chain Q99MR8
Q62000 MKIAA0718 protein (Fragment) Q80TT5 M-protein O55124 Murinoglobulin 1 precursor P28665 Myoglobin P04247
Q9JHR4 P97457
N Q62411
P Platelet glycoprotein IV Q08857
Q9JK53 Propionyl-coenzyme A carboxylase, alpha polypeptide Q80VU5 Pyruvate carboxylase Q05920 S Serine proteinase inhibitor A3K precursor P07759 Serotransferrin precursor Q921I1 Serum albumin precursor P07724 Similar to myosin, heavy polypeptide 2, skeletal muscle, adult Q922D2 T
O35452 Transthyretin precursor P07309
Q8BLW0Type VI collagen alpha 3 subunit O88493 V
Q02789
Ecto-ADP-ribosyltransferase 3 precursor
Fibromodulin precursor
Gelsolin precursor, plasma
GPI-linked NAD(P)(+)--arginine ADP-ribosyltransferase 1 precursor
Ig heavy chain V region MOPC 104E
Membrane copper amine oxidase
Mimecan precursor
Myosin heavy chain IIB Myosin regulatory light chain 2, skeletal muscle isoform
Nebulin (fragment)
Prolargin precursor
Tenascin X
Type 3 myosin heavy chain homolog (Fragment)
Voltage-dependent L-type calcium channel, alpha-1S subunit
242
Heart 1700007H16Rik protein Q9DAM43,2-trans-enoyl-CoA isomerase, mitochondrial precursor P42125 A Actin, alpha cardiac P04270 Actinin alpha 2 Q8K3Q4 Acyl coenzyme A thioester hydrolase, mitochondrial precursor Q9QYR9ADP,ATP carrier protein, heart/skeletal muscle isoform T1 P48962 Alpha-1-antitrypsin 1-1 precursor P07758 Alpha-1-antitrypsin 1-3 precursor Q00896 Alpha-2-macroglobulin precursor Q61838 Antithrombin-III precursor P32261 Aspartate aminotransferase, mitochondrial precursor P05202 ATP synthase alpha chain, mitochondrial precursor Q03265 ATP synthase B chain, mitochondrial precursor Q9CQQ7ATP synthase D chain, mitochondrial Q9DCX2B Basement membrane-specific heparan sulfate proteoglycan core protein precursor
Q05793
C Cardiac Ca2+ release channel Q9ERN6 Carnitine palmitoyltransferase I O35287 Collagen alpha 2(VI) chain precursor Q02788 Creatine kinase, M chain P07310 F Fructose-bisphosphate aldolase A P05064 G Glyceraldehyde 3-phosphate dehydrogenase P16858 H Hemoglobin alpha, adult chain 1 Q9CY10 Hemoglobin beta-1 chain P02088 Hypothetical protein Q91WP2 I Integrin alpha-7 precursor Q61738 Integrin beta-1 precursor P09055 Isocitrate dehydrogenase 2 Q8C2R9 L Laminin alpha-2 chain precursor Q60675 Laminin beta-1 chain precursor P02469 Liver carboxylesterase precursor P23953 L-lactate dehydrogenase A chain P06151 L-lactate dehydrogenase B chain P16125 Lumican precursor P51885 M Malate dehydrogenase, mitochondrial precursor P08249
Proteins marked in green were identified only in muscle extracts
243
Methylcrotonyl-CoA carboxylase alpha chain Q99MR8M-protein O55124 Myoglobin P04247 Myosin heavy chain, cardiac muscle alpha isoform Q02566 Myosin-binding protein C, cardiac-type O70468 N NADH-ubiquinone oxidoreductase chain 4 P03911 NADH-ubiquinone oxidoreductase chain 5 P03921 Neural-cadherin precursor P15116 P Peroxiredoxin 6 O08709 Platelet glycoprotein IV Q08857 Propionyl-coenzyme A carboxylase, alpha polypeptide Q80VU5 Pyruvate carboxylase Q05920 S Sarcoplasmic/endoplasmic reticulum calcium ATPase 2 O55143 Serotransferrin precursor Q921I1 Serum albumin precursor P07724 Short chain 3-hydroxyacyl-CoA dehydrogenase, mitochondrial precursor Q61425 Similar to acetyl-coenzyme A acyltransferase 2 Q8JZR8 Similar to mitochondrial aconitase Q99KI0 Solute carrier family 25, member 13 Q9QXX4T Titin homolog (Fragment) Q8BUJ0 Transthyretin precursor P07309 Trifunctional enzyme alpha subunit Q8BMS1Tubulin alpha-1 chain P05209 Type VI collagen alpha 3 subunit O88493 U Ubiquinol-cytochrome C reductase complex core protein I, mitochondrial precursor
Q9CZ13
Ubiquitin-like 1 Q9CQZ2 V Voltage-dependent anion-selective channel protein 2 Q60930 Voltage-dependent anion-selective channel protein 3 Q60931 Proteins marked in violet were identified only in heart extracts
F9 Tumor 0 day neonate head cDNA, RIKEN full-length enriched library Q9D2K8 4F2 cell-surface antigen heavy chain P10852 A Acetyl-CoA carboxylase 265 (Fragment) Q925C4 Actin, cytoplasmic 1 P02570 Alpha-1-antitrypsin 1-3 precursor Q00896 Alpha-2-macroglobulin precursor Q61838 Apolipoprotein A-I precursor Q00623
244
Apolipoprotein A-IV precursor P06728 B Basement membrane-specific heparan sulfate proteoglycan core protein precursor
Q05793
C P08122
Collagen alpha 2(VI) chain precursor Q02788 Complement C3 precursor P01027 E Elongation factor 1-alpha 1 P10126 F Fibrinogen A alpha polypeptide Q99K47 Fibrinogen, B beta polypeptide Q8K0E8 Fibronectin precursor P11276 G
P17439 Glyceraldehyde 3-phosphate dehydrogenase P16858 H Heat shock protein HSP 90-alpha P07901 Hemoglobin alpha, adult chain 1 Q9CY10 Hemoglobin beta-1 chain P02088 Histone H2B F P10853 I Inter-alpha-trypsin inhibitor heavy chain H3 precursor Q61704 L Liver carboxylesterase precursor P23953 M Methylcrotonyl-CoA carboxylase alpha chain Q99MR8
Q9QXZ0Murinoglobulin 1 precursor P28665 N Nidogen-2 precursor O88322 P Propionyl-coenzyme A carboxylase, alpha polypeptide Q80VU5 Pyruvate carboxylase Q05920 R
Q9DBK8S Serine proteinase inhibitor A3K precursor P07759 Serotransferrin precursor Q921I1 Serum albumin precursor P07724 Similar to fibrinogen, gamma polypeptide Q8VCM7
Q921X9 Similar to myosin, heavy polypeptide 2, skeletal muscle, adult Q922D2
P13641 T
Q8BKG3Transthyretin precursor P07309 Tubulin alpha-1 chain P02551
Q9D6F9
Collagen alpha 2(IV) chain precursor
Glucosylceramidase precursor
Microtubule-actin crosslinking factor 1
Repeat family 3 gene
Similar to for protein disulfide isomerase-related
Small nuclear ribonucleoprotein Sm D1
Transmembrane receptor precursor homolog
Tubulin beta-4 chain
245
Type VI collagen alpha 3 subunit O88493 Z
Q9JLD5
RENCA tumor 1200011D11Rik protein Q9DBX34F2 cell-surface antigen heavy chain P10852 60 kDa heat shock protein, mitochondrial precursor P19226 A Acetyl-CoA carboxylase 265 (Fragment) Q925C4 Actin, aortic smooth muscle P03996 Actin, cytoplasmic 1 P02570 Alcohol dehydrogenase [NADP+] Q9JII6 Alpha enolase P17182 Alpha-1-antitrypsin 1-1 precursor Q00897 Alpha-1-antitrypsin 1-3 precursor Q00896 Alpha-2-macroglobulin precursor Q61838 Aminopeptidase N P97449 Antithrombin-III precursor P32261 ARG1 Q9EQT9 Argininosuccinate synthase P16460 ATP synthase alpha chain Q03265 ATP synthase beta chain P56480 ATPase, Na+K+ transporting, alpha 1 polypeptide Q8VDN2B Basement membrane-specific heparan sulfate proteoglycan core protein precursor
Q05793
Biglycan precursor P28653 C Cadherin-16 precursor O88338 Carbonic anhydrase XII precursor Q8CI85 Collagen alpha 1(I) chain precursor P11087 Collagen alpha 1(IV) chain precursor P02463 Collagen alpha 1(VI) chain precursor Q04857 Collagen alpha 2(VI) chain precursor Q02788 Collagen type XIV precursor Q80X19 Complement C3 precursor P01027 E Elongation factor 1-alpha 1 P10126 Elongation factor 2 P58252 Esterase 10 Q9CWI4 F Fatty acid transport protein 2 O88560 Fibrinogen A alpha polypeptide Q99K47 Fibrinogen, B beta polypeptide Q8K0E8
Zinc finger protein (Fragment)
Proteins marked in orange were identified only in F9 tumor extracts
246
Fibronectin precursor P11276 G Gamma-glutamyltranspeptidase 1 precursor Q60928 Glyceraldehyde 3-phosphate dehydrogenase P16858 H H-2 class I histocompatibility antigen, K-D alpha chain precursor P01902 Hemoglobin alpha chain P01942 Hemoglobin beta-1 chain P02088 Hemoglobin beta-2 chain P02089 I Integrin alpha-3 precursor Q62470 Integrin alpha-V precursor P43406 Integrin beta-1 precursor P09055 Inter-alpha-trypsin inhibitor heavy chain H3 precursor Q61704 L Laminin alpha-5 chain precursor Q61001 Laminin beta-1 chain precursor P02469 Laminin gamma-1 chain precursor P02468 Lipoprotein receptor-related protein Q91ZX7 L-lactate dehydrogenase A chain P06151 Lutheran blood group Q99K86 M Malate dehydrogenase, mitochondrial precursor P08249 Meprin A alpha-subunit precursor P28825 Methylcrotonyl-CoA carboxylase alpha chain Q99MR8Microsomal dipeptidase precursor P31428 Monocyte differentiation antigen CD14 precursor P10810 Murinoglobulin 1 precursor P28665 N NADH-ubiquinone oxidoreductase chain 4 P03911 Nidogen precursor P10493 Nidogen-2 precursor O88322 Nonmuscle heavy chain myosin II-A Q8VDD5P Pancreas sodium bicarbonate cotransporter O88343 Plasma glutathione peroxidase precursor P46412 Propionyl-coenzyme A carboxylase, alpha polypeptide Q80VU5 Pyruvate carboxylase Q05920 S Serine proteinase inhibitor A3C precursor P29621 Serine proteinase inhibitor A3K precursor P07759 Serotransferrin precursor Q921I1 Serum albumin precursor P07724 Similar to fibrinogen, gamma polypeptide Q8VCM7Similar to mitochondrial aconitase Q99KI0 Similar to tubulointerstitial nephritis antigen Q91XG7 Sodium/potassium-transporting ATPase beta-1 chain P14094 Solute carrier family 25, member 13 Q9QXX4Solute carrier family 4 Q8BUG0T
247
T-lymphoma invasion and metastasis inducing protein 1 Q60610 Transketolase P40142 Transthyretin precursor P07309 Tubulin alpha-1 chain P02551 Tubulin beta-2 chain P05217 Type VI collagen alpha 3 subunit O88493 Type XV collagen O35206 U UDP-glucuronosyltransferase 1-1 precursor, microsomal Q63886 V Vitronectin precursor P29788 Voltage-dependent anion-selective channel protein 1 Q60932 Proteins marked in pink were identified only in RENCA tumor extracts
248