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

This page was generated automatically upon download from the ETH Zurich Research Collection. For moreinformation please consult the Terms of use.

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

2

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

3

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

4

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),

5

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.

6

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.

7

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

8

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

9

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

10

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).

11

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

12

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

13

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).

14

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

15

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).

16

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

18

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

106

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

107

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.

108

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).

110

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.

113

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,

115

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.

118

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.

119

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.

120

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.

121

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.

122

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.

123

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).

124

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

125

(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

127

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.

128

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

129

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%.

132

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 +

133

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.

134

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

136

+ -

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.

137

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,

142

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.

146

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.

147

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.

148

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.

150

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,

151

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

152

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.

153

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

155

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.

156

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).

157

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

158

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.

159

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),

163

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

164

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

165

(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

166

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.

167

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

7. References

Abba, M. C., J. A. Drake, et al. (2004). "Transcriptomic changes in human breast

cancer progression as determined by serial analysis of gene expression."

Breast Cancer Res 6(5): R499-513.

Abdulkadir, S. A., G. F. Carvalhal, et al. (2000). "Tissue factor expression and

angiogenesis in human prostate carcinoma." Hum Pathol 31(4): 443-7.

Abraham, J. A., J. L. Whang, et al. (1986). "Human basic fibroblast growth factor:

nucleotide sequence and genomic organization." Embo J. 5(10): 2523-8.

Adam, G. C., E. J. Sorensen, et al. (2002). "Chemical strategies for functional

proteomics." Mol Cell Proteomics 1(10): 781-90.

Adam, L., R. K. Vadlamudi, et al. (2001). "Tiam1 over expression potentiates

heregulin-induced lymphoid enhancer factor-1/beta -catenin nuclear signaling

in breast cancer cells by modulating the intercellular stability." J Biol Chem

276(30): 28443-50.

Aebersold, R. and M. Mann (2003). "Mass spectrometry-based proteomics." Nature

422(6928): 198-207.

Agarwal, N., C. L. Hsieh, et al. (1991). "Sequence analysis, expression and

chromosomal localization of a gene, isolated from a subtracted human retina

cDNA library, that encodes an insulin-like growth factor binding protein

(IGFBP2)." Exp. Eye Res. 52(5): 549-61.

Ahmed, N., K. Oliva, et al. (2003). "Proteomic profiling of proteins associated with

urokinase plasminogen activator receptor in a colon cancer cell line using an

antisense approach." Proteomics 3(3): 288-98.

176

Ahn, K. S., Y. S. Jung, et al. (2001). "Behavior of murine renal carcinoma cells grown

in ectopic or orthotopic sites in syngeneic mice." Tumor Biol 22(3): 146-53.

Aigner, S., Z. M. Sthoeger, et al. (1997). "CD24, a mucin-type glycoprotein, is a

ligand for P-selectin on human tumor cells." Blood 89(9): 3385-95.

Aird, W. C., J. M. Edelberg, et al. (1997). "Vascular bed-specific expression of an

endothelial cell gene is programmed by the tissue microenvironment." J Cell

Biol 138(5): 1117-24.

Alessandri, G., R. G. Chirivi, et al. (1999). "Phenotypic and functional characteristics

of tumor-derived microvascular endothelial cells." Clin Exp Metastasis 17(8):

655-62.

Allander, S. V., C. Larsson, et al. (1994). "Characterization of the chromosomal gene

and promoter for human insulin-like growth factor binding protein-5." J. Biol.

Chem. 269(14): 10891-8.

Alwine, J. C., D. J. Kemp, et al. (1977). "Method for detection of specific RNAs in

agarose gels by transfer to diazobenzyloxymethyl-paper and hybridization

with DNA probes." Proc Natl Acad Sci U S A 74(12): 5350-4.

Amado, M., R. Almeida, et al. (1998). "A family of human beta3-

galactosyltransferases. Characterization of four members of a UDP-

galactose:beta-N-acetyl-glucosamine/beta-nacetyl-galactosamine beta-1,3-

galactosyltransferase family." J. Biol. Chem. 273(21): 12770-8.

Ambrosini, G., C. Adida, et al. (1997). "A novel anti-apoptosis gene, survivin,

expressed in cancer and lymphoma." Nat. Med. 3(8): 917-21.

Ames, G. F. and K. Nikaido (1976). "Two-dimensional gel electrophoresis of

membrane proteins." Biochemistry 15(3): 616-23.

177

An, J., J. Y. Sun, et al. (2004). "Proteomics analysis of differentially expressed

metastasis-associated proteins in adenoid cystic carcinoma cell lines of human

salivary gland." Oral Oncol 40(4): 400-8.

Anderson, L. and J. Seilhamer (1997). "A comparison of selected mRNA and protein

abundances in human liver." Electrophoresis 18(3-4): 533-7.

Annunen, P., T. Helaakoski, et al. (1997). "Cloning of the human prolyl 4-

hydroxylase alpha subunit isoform alpha(II) and characterization of the type II

enzyme tetramer. The alpha(I) and alpha(II) subunits do not form a mixed

alpha(I)alpha(II)beta2 tetramer." J. Biol. Chem. 272(28): 17342-8.

Aoka, Y., F. L. Johnson, et al. (2002). "The embryonic angiogenic factor Del1

accelerates tumor growth by enhancing vascular formation." Microvasc Res

64(1): 148-61.

Arens, N., M. Gandhari, et al. (2005). "In vitro suppression of urokinase plasminogen

activator in breast cancer cells--a comparison of two antisense strategies." Int J

Oncol 26(1): 113-9.

Auerbach, R., L. Alby, et al. (1985). "Expression of organ-specific antigens on

capillary endothelial cells." Microvasc Res 29(3): 401-11.

Auerbach, W. and R. Auerbach (1994). "Angiogenesis inhibition: a review."

Pharmacol Ther 63(3): 265-311.

Austin, C. and S. Wray (1993). "Extracellular pH signals affect rat vascular tone by

rapid transduction into intracellular pH changes." J. Physiol. 466: 1-8.

Baltz, J. M. (1993). "Intracellular pH regulation in the early embryo." Bioessays

15(8): 523-30.

178

Baluk, P., S. Morikawa, et al. (2003). "Abnormalities of basement membrane on

blood vessels and endothelial sprouts in tumors." Am J Pathol 163(5): 1801-

15.

Balza, E., P. Castellani, et al. (2001). "Lack of specificity of endoglin expression for

tumor blood vessels." International Journal of Cancer 94(4): 579-585.

Bardos, H., P. Molnar, et al. (1996). "Fibrin deposition in primary and metastatic

human brain tumours." Blood Coagul Fibrinolysis 7(5): 536-48.

Barinaga, M. (1999). "New type of blood vessel found in tumors." Science 285(5433):

1475.

Bauer, M., W. Dieterich, et al. (1997). "Complete primary structure of human

collagen type XIV (undulin)." Biochim. Biophys. Acta 1354(3): 183-8.

Belaaouaj, A., J. M. Shipley, et al. (1995). "Human macrophage metalloelastase.

Genomic organization, chromosomal location, gene linkage, and tissue-

specific expression." J. Biol. Chem. 270(24): 14568-75.

Belanger, A. J., H. Lu, et al. (2002). "Hypoxia up-regulates expression of peroxisome

proliferator-activated receptor gamma angiopoietin-related gene (PGAR) in

cardiomyocytes: role of hypoxia inducible factor 1alpha." J Mol Cell Cardiol

34(7): 765-74.

Berg, J. T., E. C. Breen, et al. (1998). "Alveolar hypoxia increases gene expression of

extracellular matrix proteins and platelet-derived growth factor-B in lung

parenchyma." Am J Respir Crit Care Med 158(6): 1920-8.

Berglund, L. and T. E. Petersen (1992). "The gene structure of tetranectin, a

plasminogen binding protein." FEBS Lett. 309(1): 15-9.

Berstine, E. G., M. L. Hooper, et al. (1973). "Alkaline phosphatase activity in mouse

teratoma." Proc Natl Acad Sci U S A 70(12): 3899-903.

179

Bianchi, A., J. L. Evans, et al. (1990). "Identification of an isozymic form of acetyl-

CoA carboxylase." J Biol Chem 265(3): 1502-9.

Bicknell, R. (2002). "An update of new targets for cancer treatment: vessels and

matrix." Ann Oncol 13 Suppl 4: 25-8.

Bigner, D. D., M. T. Brown, et al. (1998). "Iodine-131-labeled antitenascin

monoclonal antibody 81C6 treatment of patients with recurrent malignant

gliomas: Phase I trial results." Journal of Clinical Oncology 16(6): 2202-2212.

Birchler, M., G. Neri, et al. (1999). "Infrared photodetection for the in vivo

localisation of phage-derived antibodies directed against angiogenic markers."

J. Immunol. Methods 231(1-2): 239-48.

Birchler, M., F. Viti, et al. (1999). "Selective targeting and photocoagulation of ocular

angiogenesis mediated by a phage-derived human antibody fragment." Nat

Biotechnol 17(10): 984-8.

Bischoff, J. (1997). "Cell adhesion and angiogenesis." J Clin Invest 99(3): 373-6.

Bissell, M. J. (1999). "Tumor plasticity allows vasculogenic mimicry, a novel form of

angiogenic switch. A rose by any other name?" Am J Pathol 155(3): 675-9.

Biswas, P., S. Canosa, et al. (2003). "PECAM-1 promotes beta-catenin accumulation

and stimulates endothelial cell proliferation." Biochem Biophys Res Commun

303(1): 212-8.

Blood, C. H. and B. R. Zetter (1990). "Tumor interactions with the vasculature:

angiogenesis and tumor metastasis." Biochim Biophys Acta 1032(1): 89-118.

Boon, K., E. C. Osorio, et al. (2002). "An anatomy of normal and malignant gene

expression." Proc Natl Acad Sci U S A 99(17): 11287-92.

180

Borsi, L., G. Allemanni, et al. (1996). "Extracellular pH controls pre-mRNA

alternative splicing of tenascin-C in normal, but not in malignantly

transformed, cells." Int. J. Cancer 66(5): 632-5.

Borsi, L., E. Balza, et al. (2002). "Selective targeting of tumoral vasculature:

comparison of different formats of an antibody (L19) to the ED-B domain of

fibronectin." Int J Cancer 102(1): 75-85.

Borsi, L., E. Balza, et al. (2003). "Selective targeted delivery of TNFalpha to tumor

blood vessels." Blood 102(13): 4384-92.

Borsi, L., E. Balza, et al. (1994). "Cell-cycle dependent alternative splicing of the

tenascin primary transcript." Cell Adhes. Commun. 1(4): 307-17.

Borsi, L., E. Balza, et al. (1995). "The alternative splicing pattern of the tenascin-C

pre-mRNA is controlled by the extracellular pH." J. Biol. Chem. 270(11):

6243-5.

Borsi, L., B. Carnemolla, et al. (1992). "Expression of different tenascin isoforms in

normal, hyperplastic and neoplastic human breast tissues." Int. J. Cancer

52(5): 688-92.

Bosslet, K., R. Straub, et al. (1998). "Elucidation of the mechanism enabling tumor

selective prodrug monotherapy." Cancer Res 58(6): 1195-201.

Brack, S. S., L. M. Dinkelborg, et al. (2004). "Molecular targeting of angiogenesis for

imaging and therapy." Eur J Nucl Med Mol Imaging 31(9): 1327-41.

Braddock, M., J. L. Schwachtgen, et al. (1998). "Fluid Shear Stress Modulation of

Gene Expression in Endothelial Cells." News Physiol Sci 13: 241-246.

Brandli, A. W., R. G. Parton, et al. (1990). "Transcytosis in MDCK cells:

identification of glycoproteins transported bidirectionally between both

plasma membrane domains." J Cell Biol 111(6 Pt 2): 2909-21.

181

Bredow, S., M. Lewin, et al. (2000). "Imaging of tumour neovasculature by targeting

the TGF-[beta] binding receptor endoglin." European Journal of Cancer 36(5):

675-681.

Brekken, R. A., X. Huang, et al. (1998). "Vascular endothelial growth factor as a

marker of tumor endothelium." Cancer Res 58(9): 1952-9.

Brekken, R. A., J. P. Overholser, et al. (2000). "Selective inhibition of vascular

endothelial growth factor (VEGF) receptor 2 (KDR/Flk-1) activity by a

monoclonal anti-VEGF antibody blocks tumor growth in mice." Cancer

Research 60(18): 5117-5124.

Brody, J. S., H. Kagan, et al. (1979). "Lung lysyl oxidase activity: relation to lung

growth." Am Rev Respir Dis 120(6): 1289-95.

Brooks, P. C., A. M. Montgomery, et al. (1994). "Integrin alpha v beta 3 antagonists

promote tumor regression by inducing apoptosis of angiogenic blood vessels."

Cell 79(7): 1157-64.

Brooks, P. C., S. Silletti, et al. (1998). "Disruption of angiogenesis by PEX, a

noncatalytic metalloproteinase fragment with integrin binding activity." Cell

92(3): 391-400.

Brown, L. F., L. Van de Water, et al. (1988). "Fibrinogen influx and accumulation of

cross-linked fibrin in healing wounds and in tumor stroma." Am J Pathol

130(3): 455-65.

Brown, P. O. and D. Botstein (1999). "Exploring the new world of the genome with

DNA microarrays." Nat Genet 21(1 Suppl): 33-7.

Brunner, N., H. J. Nielsen, et al. (1999). "The urokinase plasminogen activator

receptor in blood from healthy individuals and patients with cancer." Apmis

107(1): 160-7.

182

Bucki, R., P. A. Janmey, et al. (2001). "Involvement of phosphatidylinositol 4,5-

bisphosphate in phosphatidylserine exposure in platelets: Use of a permeant

phosphoinositide-binding peptide." Biochemistry 40(51): 15752-15761.

Budanov, A. V., T. Shoshani, et al. (2002). "Identification of a novel stress-responsive

gene Hi95 involved in regulation of cell viability." Oncogene 21(39): 6017-31.

Buhring, H. J., C. A. Muller, et al. (1991). "Endoglin is expressed on a subpopulation

of immature erythroid cells of normal human bone marrow." Leukemia 5(10):

841-7.

Bumke, M. A., D. Neri, et al. (2003). "Modulation of gene expression by extracellular

pH variations in human fibroblasts: A transcriptomic and proteomic study."

Proteomics 3(5): 675-88.

Burrows, F. J., E. J. Derbyshire, et al. (1995). "Up-regulation of endoglin on vascular

endothelial cells in human solid tumors: implications for diagnosis and

therapy." Clin. Cancer Res. 1(12): 1623-34.

Burton, W. G., K. D. Nugent, et al. (1988). "Separation of proteins by reversed-phase

high-performance liquid chromatography. I. Optimizing the column." J

Chromatogr 443: 363-79.

Busch, G., D. Hoder, et al. (1989). "Selective isolation of individual cell surface

proteins from tissue culture cells by a cleavable biotin label." Eur J Cell Biol

50(2): 257-62.

Bustin, S. A. (2000). "Absolute quantification of mRNA using real-time reverse

transcription polymerase chain reaction assays." J Mol Endocrinol 25(2): 169-

93.

183

Bustin, S. A. and S. Dorudi (2004). "Gene expression profiling for molecular staging

and prognosis prediction in colorectal cancer." Expert Rev Mol Diagn 4(5):

599-607.

Byers, R. J., J. A. Hoyland, et al. (2000). "Subtractive hybridization--genetic

takeaways and the search for meaning." Int J Exp Pathol 81(6): 391-404.

Campochiaro, P. A. (2000). "Retinal and choroidal neovascularization." J Cell Physiol

184(3): 301-10.

Carmeliet, P. (2000). "Mechanisms of angiogenesis and arteriogenesis." Nat Med

6(4): 389-95.

Carmeliet, P., Y. Dor, et al. (1998). "Role of HIF-1alpha in hypoxia-mediated

apoptosis, cell proliferation and tumour angiogenesis." Nature 394(6692):

485-90.

Carmeliet, P. and R. K. Jain (2000). "Angiogenesis in cancer and other diseases."

Nature 407(6801): 249-57.

Carnemolla, B., E. Balza, et al. (1989). "A tumor-associated fibronectin isoform

generated by alternative splicing of messenger RNA precursors." J. Cell Biol.

108(3): 1139-48.

Carnemolla, B., L. Borsi, et al. (2002). "Enhancement of the antitumor properties of

interleukin-2 by its targeted delivery to the tumor blood vessel extracellular

matrix." Blood 99(5): 1659-65.

Carnemolla, B., P. Castellani, et al. (1999). "Identification of a glioblastoma-

associated tenascin-C isoform by a high affinity recombinant antibody." Am.

J. Pathol. 154(5): 1345-52.

184

Carnemolla, B., D. Neri, et al. (1996). "Phage antibodies with pan-species recognition

of the oncofoetal angiogenesis marker fibronectin ED-B domain." Int. J.

Cancer 68(3): 397-405.

Carson-Walter, E. B., D. N. Watkins, et al. (2001). "Cell surface tumor endothelial

markers are conserved in mice and humans." Cancer Res 61(18): 6649-55.

Carver, L. A. and J. E. Schnitzer (2003). "Caveolae: mining little caves for new

cancer targets." Nat Rev Cancer 3(8): 571-81.

Castellani, P., L. Borsi, et al. (2002). "Differentiation between high- and low-grade

astrocytoma using a human recombinant antibody to the extra domain-B of

fibronectin." Am J Pathol 161(5): 1695-700.

Castellani, P., G. Viale, et al. (1994). "The fibronectin isoform containing the ED-B

oncofetal domain: a marker of angiogenesis." Int. J. Cancer 59(5): 612-8.

Celis, J. E., P. Celis, et al. (1999). "Proteomics and immunohistochemistry define

some of the steps involved in the squamous differentiation of the bladder

transitional epithelium: a novel strategy for identifying metaplastic lesions."

Cancer Res 59(12): 3003-9.

Celis, J. E., P. Celis, et al. (2002). "Proteomic strategies to reveal tumor heterogeneity

among urothelial papillomas." Mol Cell Proteomics 1(4): 269-79.

Celis, J. E., M. Ostergaard, et al. (1996). "Loss of adipocyte-type fatty acid binding

protein and other protein biomarkers is associated with progression of human

bladder transitional cell carcinomas." Cancer Res 56(20): 4782-90.

Celis, J. E., H. H. Rasmussen, et al. (1996). "Bladder squamous cell carcinomas

express psoriasin and externalize it to the urine." J Urol 155(6): 2105-12.

185

Chang, C., J. Kokontis, et al. (1989). "Isolation and characterization of human TR3

receptor: a member of steroid receptor superfamily." J. Steroid Biochem. 34(1-

6): 391-5.

Chang, S. S., D. S. O'Keefe, et al. (1999). "Prostate-specific membrane antigen is

produced in tumor-associated neovasculature." Clin. Cancer Res. 5(10): 2674-

81.

Chang, Y. S., E. di Tomaso, et al. (2000). "Mosaic blood vessels in tumors: frequency

of cancer cells in contact with flowing blood." Proc Natl Acad Sci U S A

97(26): 14608-13.

Chase, H. B. (1954). "Growth of the hair." Physiol Rev 34(1): 113-26.

Chen, C. P., Y. C. Yang, et al. (2004). "Hypoxia and TGF-beta 1 act independently to

increase extracellular matrix production by placental fibroblasts." J Clin

Endocrinol Metab.

Chi, J. T., H. Y. Chang, et al. (2003). "Endothelial cell diversity revealed by global

expression profiling." Proc Natl Acad Sci U S A 100(19): 10623-8.

Cioffi, C. L., X. Q. Liu, et al. (2003). "Differential regulation of HIF-1 alpha prolyl-4-

hydroxylase genes by hypoxia in human cardiovascular cells." Biochem

Biophys Res Commun 303(3): 947-53.

Clark, E. A., T. R. Golub, et al. (2000). "Genomic analysis of metastasis reveals an

essential role for RhoC." Nature 406(6795): 532-5.

Clark, R. A., J. M. Lanigan, et al. (1982). "Fibronectin and fibrin provide a

provisional matrix for epidermal cell migration during wound

reepithelialization." J Invest Dermatol 79(5): 264-9.

Cleaver, O. and D. A. Melton (2003). "Endothelial signaling during development."

Nat Med 9(6): 661-8.

186

Collin, O. and A. Bergh (1996). "Leydig cells secrete factors which increase vascular

permeability and endothelial cell proliferation." Int J Androl 19(4): 221-8.

Comin-Anduix, B., J. Boren, et al. (2001). "The effect of thiamine supplementation on

tumour proliferation. A metabolic control analysis study." Eur J Biochem

268(15): 4177-82.

Cooke, S. P., G. M. Boxer, et al. (2001). "A strategy for antitumor vascular therapy by

targeting the vascular endothelial growth factor: Receptor complex." Cancer

Research 61(9): 3653-3659.

Cormier-Regard, S., S. V. Nguyen, et al. (1998). "Adrenomedullin gene expression is

developmentally regulated and induced by hypoxia in rat ventricular cardiac

myocytes." J Biol Chem 273(28): 17787-92.

Corthals, G. L., V. C. Wasinger, et al. (2000). "The dynamic range of protein

expression: a challenge for proteomic research." Electrophoresis 21(6): 1104-

15.

Costantini, V., L. R. Zacharski, et al. (1991). "Fibrinogen deposition without thrombin

generation in primary human breast cancer tissue." Cancer Res 51(1): 349-53.

Costantini, V., L. R. Zacharski, et al. (1992). "Fibrinogen deposition and macrophage-

associated fibrin formation in malignant and nonmalignant lymphoid tissue." J

Lab Clin Med 119(2): 124-31.

Coussens, L. M., W. W. Raymond, et al. (1999). "Inflammatory mast cells up-regulate

angiogenesis during squamous epithelial carcinogenesis." Genes Dev 13(11):

1382-97.

Craven, R. A., N. Totty, et al. (2002). "Laser capture microdissection and two-

dimensional polyacrylamide gel electrophoresis: evaluation of tissue

preparation and sample limitations." Am J Pathol 160(3): 815-22.

187

Cruet-Hennequart, S., S. Maubant, et al. (2003). "alpha(v) integrins regulate cell

proliferation through integrin-linked kinase (ILK) in ovarian cancer cells."

Oncogene 22(11): 1688-702.

Czarny, M., J. Liu, et al. (2003). "Transient mechanoactivation of neutral

sphingomyelinase in caveolae to generate ceramide." J Biol Chem 278(7):

4424-30.

Damert, A., M. Machein, et al. (1997). "Up-regulation of vascular endothelial growth

factor expression in a rat glioma is conferred by two distinct hypoxia-driven

mechanisms." Cancer Res 57(17): 3860-4.

de Fraipont, F., M. El Atifi, et al. (2004). "Gene expression profiling of human

adrenocortical tumors using cDNA microarrays identifies several candidate

genes as markers of malignancy." J Clin Endocrinol Metab.

De La Fuente, E. K., C. A. Dawson, et al. (1997). "Biotinylation of membrane

proteins accessible via the pulmonary circulation in normal and hyperoxic

rats." Am J Physiol 272(3 Pt 1): L461-70.

Demartis, S., L. Tarli, et al. (2001). "Selective targeting of tumour neovasculature by

a radiohalogenated human antibody fragment specific for the ED-B domain of

fibronectin." Eur J Nucl Med 28(4): 534-9.

Denekamp, J. (1982). "Endothelial cell proliferation as a novel approach to targeting

tumour therapy." Br J Cancer 45(1): 136-9.

Dentler, W. L. (1995). "Nonradioactive methods for labeling and identifying

membrane surface proteins." Methods Cell Biol 47: 407-11.

Diatchenko, L., Y. F. Lau, et al. (1996). "Suppression subtractive hybridization: a

method for generating differentially regulated or tissue-specific cDNA probes

and libraries." Proc Natl Acad Sci U S A 93(12): 6025-30.

188

Dibbens, J. A., D. L. Miller, et al. (1999). "Hypoxic regulation of vascular endothelial

growth factor mRNA stability requires the cooperation of multiple RNA

elements." Mol Biol Cell 10(4): 907-19.

Donati, M. B. and A. Falanga (2001). "Pathogenetic mechanisms of thrombosis in

malignancy." Acta Haematol 106(1-2): 18-24.

Dougherty, E. R., J. Barrera, et al. (2002). "Inference from clustering with application

to gene-expression microarrays." J. Comput. Biol. 9(1): 105-26.

Droz, D., N. Patey, et al. (1994). "Composition of extracellular matrix and distribution

of cell adhesion molecules in renal cell tumors." Lab Invest 71(5): 710-8.

Duffy, M. J. (2002). "Urokinase plasminogen activator and its inhibitor, PAI-1, as

prognostic markers in breast cancer: from pilot to level 1 evidence studies."

Clin Chem 48(8): 1194-7.

Durmowicz, A. G., W. C. Parks, et al. (1994). "Persistence, re-expression, and

induction of pulmonary arterial fibronectin, tropoelastin, and type I

procollagen mRNA expression in neonatal hypoxic pulmonary hypertension."

Am J Pathol 145(6): 1411-20.

Durr, E., J. Yu, et al. (2004). "Direct proteomic mapping of the lung microvascular

endothelial cell surface in vivo and in cell culture." Nat Biotechnol 22(8): 985-

92.

Dvorak, H. F. (1986). "Tumors: wounds that do not heal. Similarities between tumor

stroma generation and wound healing." N Engl J Med 315(26): 1650-9.

Dvorak, H. F., G. R. Dickersin, et al. (1981). "Human breast carcinoma: fibrin

deposits and desmoplasia. Inflammatory cell type and distribution.

Microvasculature and infarction." J Natl Cancer Inst 67(2): 335-45.

189

Dvorak, H. F., V. S. Harvey, et al. (1987). "Fibrin containing gels induce

angiogenesis. Implications for tumor stroma generation and wound healing."

Lab Invest 57(6): 673-86.

Dvorak, H. F., J. A. Nagy, et al. (1992). "Vascular permeability factor, fibrin, and the

pathogenesis of tumor stroma formation." Ann N Y Acad Sci 667: 101-11.

Dvorak, H. F., D. R. Senger, et al. (1983). "Fibrin as a component of the tumor

stroma: origins and biological significance." Cancer Metastasis Rev 2(1): 41-

73.

Easty, D. J., S. P. Hill, et al. (1999). "Up-regulation of ephrin-A1 during melanoma

progression." Int J Cancer 84(5): 494-501.

Eberhard, A., S. Kahlert, et al. (2000). "Heterogeneity of angiogenesis and blood

vessel maturation in human tumors: implications for antiangiogenic tumor

therapies." Cancer Res 60(5): 1388-93.

Ebert, B. L., J. D. Firth, et al. (1995). "Hypoxia and mitochondrial inhibitors regulate

expression of glucose transporter-1 via distinct Cis-acting sequences." J Biol

Chem 270(49): 29083-9.

Eisen, M. B., P. T. Spellman, et al. (1998). "Cluster analysis and display of genome-

wide expression patterns." Proc. Natl. Acad. Sci. U. S. A. 95(25): 14863-8.

Elia, G., M. Silacci, et al. (2002). "Visualizing the proteome in structurally intact

samples." BioWorld 3: 16-17.

Elia, G., M. Silacci, et al. (2002). "Affinity-capture reagents for protein arrays."

Trends Biotechnol 20(12 Suppl): S19-22.

Eng, J. K., A. L. McCormack, et al. (1994). "An Approach to Correlate Tandem Mass

Spectral Data of Peptides with Amino Acid Sequences in a Protein Database."

J Am Soc Mass Spectrom 5: 976-989.

190

Evtimova, V., R. Zeillinger, et al. (2003). "Identification of genes associated with the

invasive status of human mammary carcinoma cell lines by transcriptional

profiling." Tumour Biol 24(4): 189-98.

Falanga, V., T. A. Martin, et al. (1993). "Low oxygen tension increases mRNA levels

of alpha 1 (I) procollagen in human dermal fibroblasts." J Cell Physiol 157(2):

408-12.

Favaloro, E. J. (1993). "Differential expression of surface antigens on activated

endothelium." Immunol Cell Biol 71 ( Pt 6): 571-81.

Feldser, D., F. Agani, et al. (1999). "Reciprocal positive regulation of hypoxia-

inducible factor 1alpha and insulin-like growth factor 2." Cancer Res 59(16):

3915-8.

Ferrajoli, A., S. O'Brien, et al. (2001). "Alemtuzumab: a novel monoclonal antibody."

Expert Opin Biol Ther 1(6): 1059-65.

Ferrara, N. (2002). "Role of vascular endothelial growth factor in physiologic and

pathologic angiogenesis: therapeutic implications." Seminars In Oncology

29(6, Supplement 16): 10-14.

Ferry, R. J., Jr., R. W. Cerri, et al. (1999). "Insulin-like growth factor binding

proteins: new proteins, new functions." Horm. Res. 51(2): 53-67.

Fink, T., P. Ebbesen, et al. (2001). "Quantitative gene expression profiles of human

liver-derived cell lines exposed to moderate hypoxia." Cell Physiol Biochem

11(2): 105-14.

Firth, J. D., B. L. Ebert, et al. (1994). "Oxygen-regulated control elements in the

phosphoglycerate kinase 1 and lactate dehydrogenase A genes: similarities

with the erythropoietin 3' enhancer." Proc Natl Acad Sci U S A 91(14): 6496-

500.

191

Fischer, G. F., O. Majdic, et al. (1990). "Signal transduction in lymphocytic and

myeloid cells via CD24, a new member of phosphoinositol-anchored

membrane molecules." J Immunol 144(2): 638-41.

Folkman, J. (1971). "Tumor angiogenesis: therapeutic implications." N. Engl. J. Med.

285(21): 1182-6.

Folkman, J. (1972). "Anti-angiogenesis: new concept for therapy of solid tumors."

Ann Surg 175(3): 409-16.

Folkman, J. (1995). "Angiogenesis in cancer, vascular, rheumatoid and other disease."

Nat Med 1(1): 27-31.

Folkman, J. (1995). "Clinical applications of research on angiogenesis." Seminars in

medicine of the Beth Israel Hospital Boston 333: , 1757-1763.

Folkman, J. (2001). "Can mosaic tumor vessels facilitate molecular diagnosis of

cancer?" Proc Natl Acad Sci U S A 98(2): 398-400.

Fonsatti, E., A. P. Jekunen, et al. (2000). "Endoglin is a suitable target for efficient

imaging of solid tumors: In vivo evidence in a canine mammary carcinoma

model." Clinical Cancer Research 6(5): 2037-2043.

Forbush, B., 3rd, J. H. Kaplan, et al. (1978). "Characterization of a new photoaffinity

derivative of ouabain: labeling of the large polypeptide and of a proteolipid

component of the Na, K-ATPase." Biochemistry 17(17): 3667-76.

Francis, J. L. and A. Amirkhosravi (2002). "Effect of antihemostatic agents on

experimental tumor dissemination." Semin Thromb Hemost 28(1): 29-38.

Freeman, W. J. (1998). "The neurobiology of multimodal sensory integration." Integr

Physiol Behav Sci 33(2): 124-9.

192

Friedberg, J. W. and R. I. Fisher (2004). "Iodine-131 tositumomab (Bexxar):

radioimmunoconjugate therapy for indolent and transformed B-cell non-

Hodgkin's lymphoma." Expert Rev Anticancer Ther 4(1): 18-26.

Friedlander, M., P. C. Brooks, et al. (1995). "Definition of two angiogenic pathways

by distinct alpha v integrins." Science 270(5241): 1500-2.

Friedlander, M., C. L. Theesfeld, et al. (1996). "Involvement of integrins

[alpha](v)[beta]3 and [alpha](v)[beta]5 in ocular neovascular diseases."

Proceedings of the National Academy of Sciences of the United States of

America 93(18): 9764-9769.

Fujiwara, Y., H. Ohata, et al. (1995). "Isolation of a candidate tumor suppressor gene

on chromosome 8p21.3-p22 that is homologous to an extracellular domain of

the PDGF receptor beta gene." Oncogene 10(5): 891-5.

Fujiwara, Y., Y. Sugita, et al. (2000). "Assessment of Stanniocalcin-1 mRNA as a

molecular marker for micrometastases of various human cancers." Int. J.

Oncol. 16(4): 799-804.

Furukawa, Y. (2002). "Cell cycle control genes and hematopoietic cell

differentiation." Leuk. Lymphoma 43(2): 225-31.

Gale, A. J. and S. G. Gordon (2001). "Update on tumor cell procoagulant factors."

Acta Haematol 106(1-2): 25-32.

Ganesh, S., C. F. Sier, et al. (1994). "Urokinase receptor and colorectal cancer

survival." Lancet 344(8919): 401-2.

Geoghegan, K. F. and M. A. Kelly (2004). "Biochemical applications of mass

spectrometry in pharmaceutical drug discovery." Mass Spectrom Rev.

193

Gerber, H. P., F. Condorelli, et al. (1997). "Differential transcriptional regulation of

the two vascular endothelial growth factor receptor genes. Flt-1, but not Flk-

1/KDR, is up-regulated by hypoxia." J Biol Chem 272(38): 23659-67.

Gerritsen, M. E., R. Soriano, et al. (2002). "In silico data filtering to identify new

angiogenesis targets from a large in vitro gene profiling data set." Physiol

Genomics 10(1): 13-20.

Gevaert, K., B. Ghesquiere, et al. (2004). "Reversible labeling of cysteine-containing

peptides allows their specific chromatographic isolation for non-gel proteome

studies." Proteomics 4(4): 897-908.

Gevaert, K., M. Goethals, et al. (2003). "Exploring proteomes and analyzing protein

processing by mass spectrometric identification of sorted N-terminal

peptides." Nat Biotechnol 21(5): 566-9.

Gevaert, K., J. Van Damme, et al. (2002). "Chromatographic isolation of methionine-

containing peptides for gel-free proteome analysis: identification of more than

800 Escherichia coli proteins." Mol Cell Proteomics 1(11): 896-903.

Gianazza, E., T. Rabilloud, et al. (1987). "Additives for immobilized pH gradient two-

dimensional separation of particulate material: comparison between

commercial and new synthetic detergents." Anal Biochem 165(2): 247-57.

Gilbert, S. F. (2000). Developmental biology. Sunderland, MA, Sinauer Associates,

Inc.

Giordano, F. J. and R. S. Johnson (2001). "Angiogenesis: the role of the

microenvironment in flipping the switch." Curr Opin Genet Dev 11(1): 35-40.

Giovannoni, L., F. Viti, et al. (2001). "Isolation of anti-angiogenesis antibodies from a

large combinatorial repertoire by colony filter screening." Nucleic Acids Res

29(5): E27.

194

Goldenberg, M. M. (1999). "Trastuzumab, a recombinant DNA-derived humanized

monoclonal antibody, a novel agent for the treatment of metastatic breast

cancer." Clin Ther 21(2): 309-18.

Gonzalez, N. C. and J. G. Wood (2001). "Leukocyte-endothelial interactions in

environmental hypoxia." Adv Exp Med Biol 502: 39-60.

Goonewardene, T. I., H. M. Sowter, et al. (2002). "Hypoxia-induced pathways in

breast cancer." Microsc Res Tech 59(1): 41-8.

Gorbunoff, M. J. (1984). "The interaction of proteins with hydroxyapatite. I. Role of

protein charge and structure." Anal Biochem 136(2): 425-32.

Gorbunoff, M. J. (1984). "The interaction of proteins with hydroxyapatite. II. Role of

acidic and basic groups." Anal Biochem 136(2): 433-9.

Gorbunoff, M. J. and S. N. Timasheff (1984). "The interaction of proteins with

hydroxyapatite. III. Mechanism." Anal Biochem 136(2): 440-5.

Gorg, A., W. Postel, et al. (1988). "The current state of two-dimensional

electrophoresis with immobilized pH gradients." Electrophoresis 9(9): 531-46.

Grand, R. J., A. E. Milner, et al. (1995). "A novel protein expressed in mammalian

cells undergoing apoptosis." Exp. Cell Res. 218(2): 439-51.

Grillo-Lopez, A. J. (2002). "Zevalin: the first radioimmunotherapy approved for the

treatment of lymphoma." Expert Rev Anticancer Ther 2(5): 485-93.

Grimm, C., A. Wenzel, et al. (2002). "HIF-1-induced erythropoietin in the hypoxic

retina protects against light-induced retinal degeneration." Nat Med 8(7): 718-

24.

Gullino, P. M. (1981). "Angiogenesis and neoplasia." N Engl J Med 305(15): 884-5.

195

Gupta, S. K., T. Hassel, et al. (1995). "A potent inhibitor of endothelial cell

proliferation is generated by proteolytic cleavage of the chemokine platelet

factor 4." Proc Natl Acad Sci U S A 92(17): 7799-803.

Gutheil, J. C., T. N. Campbell, et al. (2000). "Targeted antiangiogenic therapy for

cancer using Vitaxin: a humanized monoclonal antibody to the integrin

alphavbeta3." Clin Cancer Res 6(8): 3056-61.

Gygi, S. P., G. L. Corthals, et al. (2000). "Evaluation of two-dimensional gel

electrophoresis-based proteome analysis technology." Proc Natl Acad Sci U S

A 97(17): 9390-5.

Gygi, S. P., B. Rist, et al. (1999). "Quantitative analysis of complex protein mixtures

using isotope-coded affinity tags." Nat Biotechnol 17(10): 994-9.

Gygi, S. P., Y. Rochon, et al. (1999). "Correlation between protein and mRNA

abundance in yeast." Mol. Cell. Biol. 19(3): 1720-30.

Ha, V. T., M. K. Marshall, et al. (1994). "A patient with Ehlers-Danlos syndrome type

VI is a compound heterozygote for mutations in the lysyl hydroxylase gene." J

Clin Invest 93(4): 1716-21.

Halin, C., V. Gafner, et al. (2003). "Synergistic therapeutic effects of a tumor

targeting antibody fragment, fused to interleukin 12 and to tumor necrosis

factor alpha." Cancer Res 63(12): 3202-10.

Halin, C., U. Niesner, et al. (2002). "Tumor-targeting properties of antibody-vascular

endothelial growth factor fusion proteins." Int J Cancer 102(2): 109-16.

Halin, C., S. Rondini, et al. (2002). "Enhancement of the antitumor activity of

interleukin-12 by targeted delivery to neovasculature." Nat. Biotechnol. 20(3):

264-9.

196

Hamada, K., J. Kuratsu, et al. (1996). "Expression of tissue factor correlates with

grade of malignancy in human glioma." Cancer 77(9): 1877-83.

Hammond, E. M., N. C. Denko, et al. (2002). "Hypoxia links ATR and p53 through

replication arrest." Mol Cell Biol 22(6): 1834-43.

Han, D. K., J. Eng, et al. (2001). "Quantitative profiling of differentiation-induced

microsomal proteins using isotope-coded affinity tags and mass spectrometry."

Nat Biotechnol 19(10): 946-51.

Hanahan, D. (1998). "A flanking attack on cancer." Nat Med 4(1): 13-4.

Hanahan, D., G. Christofori, et al. (1996). "Transgenic mouse models of tumour

angiogenesis: the angiogenic switch, its molecular controls, and prospects for

preclinical therapeutic models." Eur J Cancer 32A(14): 2386-93.

Hangai, M., N. Kitaya, et al. (2002). "Matrix metalloproteinase-9-dependent exposure

of a cryptic migratory control site in collagen is required before retinal

angiogenesis." Am J Pathol 161(4): 1429-37.

Hapke, S., H. Kessler, et al. (2003). "Ovarian cancer cell proliferation and motility is

induced by engagement of integrin alpha(v)beta3/Vitronectin interaction."

Biol Chem 384(7): 1073-83.

Harder, A., R. Wildgruber, et al. (1999). "Comparison of yeast cell protein

solubilization procedures for two-dimensional electrophoresis."

Electrophoresis 20(4-5): 826-9.

Hardy, M. H. (1992). "The secret life of the hair follicle." Trends Genet 8(2): 55-61.

Haubner, R., H.-J. Wester, et al. (2001). "Glycosylated RGD-containing peptides:

Tracer for tumor targeting and angiogenesis imaging with improved

biokinetics." Journal of Nuclear Medicine 42(2): 326-336.

197

Haubner, R., H. J. Wester, et al. (2001). "Noninvasive imaging of [alpha]v[beta]3

integrin expression using F-labeled RGD-containing glycopeptide and

positron emission tomography." Cancer Research 61(5): 1781-1785.

Haynes, P. A. and J. R. Yates, 3rd (2000). "Proteome profiling-pitfalls and progress."

Yeast 17(2): 81-7.

Helbling, P. M., D. M. Saulnier, et al. (2000). "The receptor tyrosine kinase EphB4

and ephrin-B ligands restrict angiogenic growth of embryonic veins in

Xenopus laevis." Development 127(2): 269-78.

Helfman, T. and V. Falanga (1993). "Gene expression in low oxygen tension." Am J

Med Sci 306(1): 37-41.

Helmlinger, G., F. Yuan, et al. (1997). "Interstitial pH and pO2 gradients in solid

tumors in vivo: high-resolution measurements reveal a lack of correlation."

Nat Med 3(2): 177-82.

Herzenberg, L. A., D. Parks, et al. (2002). "The history and future of the fluorescence

activated cell sorter and flow cytometry: a view from Stanford." Clin Chem

48(10): 1819-27.

Hidai, C., T. Zupancic, et al. (1998). "Cloning and characterization of developmental

endothelial locus-1: an embryonic endothelial cell protein that binds the

alphavbeta3 integrin receptor." Genes Dev 12(1): 21-33.

Hindermann, W., A. Berndt, et al. (1999). "Synthesis and protein distribution of the

unspliced large tenascin-C isoform in oral squamous cell carcinoma." J Pathol

189(4): 475-80.

Hirakawa, S., Y. K. Hong, et al. (2003). "Identification of vascular lineage-specific

genes by transcriptional profiling of isolated blood vascular and lymphatic

endothelial cells." Am J Pathol 162(2): 575-86.

198

Hjerten, S., O. Levin, et al. (1956). "Protein chromatography on calcium phosphate

columns." Arch Biochem Biophys 65(1): 132-55.

Holder, N. and R. Klein (1999). "Eph receptors and ephrins: effectors of

morphogenesis." Development 126(10): 2033-44.

Holland, C. M., S. A. Saidi, et al. (2004). "Transcriptome analysis of endometrial

cancer identifies peroxisome proliferator-activated receptors as potential

therapeutic targets." Mol Cancer Ther 3(8): 993-1001.

Holmes, E. H. (2001). "PSMA specific antibodies and their diagnostic and therapeutic

use." Expert Opinion on Investigational Drugs 10(3): 511-519.

Holmes, W. E., M. X. Sliwkowski, et al. (1992). "Identification of heregulin, a

specific activator of p185erbB2." Science 256(5060): 1205-10.

Hoogenboom, H. R., J. T. Lutgerink, et al. (1999). "Selection-dominant and

nonaccessible epitopes on cell-surface receptors revealed by cell-panning with

a large phage antibody library." Eur. J. Biochem. 260(3): 774-784.

Horigome, T., T. Hiranuma, et al. (1989). "Ceramic hydroxyapatite high-performance

liquid chromatography of complexes membrane protein and sodium

dodecylsulfate." Eur J Biochem 186(1-2): 63-9.

Huber, A. H. and W. I. Weis (2001). "The structure of the beta-catenin/E-cadherin

complex and the molecular basis of diverse ligand recognition by beta-

catenin." Cell 105(3): 391-402.

Huminiecki, L., M. Gorn, et al. (2002). "Magic roundabout is a new member of the

roundabout receptor family that is endothelial specific and expressed at sites

of active angiogenesis." Genomics 79(4): 547-52.

199

Huston, J. S., D. Levinson, et al. (1988). "Protein engineering of antibody binding

sites: recovery of specific activity in an anti-digoxin single-chain Fv analogue

produced in Escherichia coli." Proc Natl Acad Sci U S A 85(16): 5879-83.

Ichimura, T., N. Ikuta, et al. (1995). "Separation of membrane proteins solubilized

with a nondenaturing detergent and a high salt concentration by

hydroxyapatite high-performance liquid chromatography." Anal Biochem

224(1): 250-5.

Ikegawa, S., T. Toda, et al. (1996). "Structure and chromosomal assignment of the

human S1-5 gene (FBNL) that is highly homologous to fibrillin." Genomics

35(3): 590-2.

Inohara, N., T. Koseki, et al. (1997). "CLARP, a death effector domain-containing

protein interacts with caspase-8 and regulates apoptosis." Proc. Natl. Acad.

Sci. U. S. A. 94(20): 10717-22.

Ishibashi, K. and M. Imai (2002). "Prospect of a stanniocalcin endocrine/paracrine

system in mammals." Am. J. Physiol. Renal Physiol. 282(3): F367-75.

Iverson, A. J., A. Bianchi, et al. (1990). "Immunological analysis of acetyl-CoA

carboxylase mass, tissue distribution and subunit composition." Biochem J

269(2): 365-71.

Iyer, N. V., L. E. Kotch, et al. (1998). "Cellular and developmental control of O2

homeostasis by hypoxia-inducible factor 1 alpha." Genes Dev 12(2): 149-62.

Iyer, V. R., M. B. Eisen, et al. (1999). "The transcriptional program in the response of

human fibroblasts to serum." Science 283(5398): 83-7.

Jacobson, B. S., J. E. Schnitzer, et al. (1992). "Isolation and partial characterization of

the luminal plasmalemma of microvascular endothelium from rat lungs." Eur J

Cell Biol 58(2): 296-306.

200

Jain, R. K. (2003). "Molecular regulation of vessel maturation." Nat Med 9(6): 685-

93.

James, J. S. and G. Dubs (1997). "FDA approves new kind of lymphoma treatment.

Food and Drug Administration." AIDS Treat News(No 284): 2-3.

James, P., M. Quadroni, et al. (1994). "Protein identification in DNA databases by

peptide mass fingerprinting." Protein Sci 3(8): 1347-50.

Jessani, N., M. Humphrey, et al. (2004). "Carcinoma and stromal enzyme activity

profiles associated with breast tumor growth in vivo." Proc Natl Acad Sci U S

A 101(38): 13756-61.

Joseph, I. B. and J. T. Isaacs (1997). "Potentiation of the antiangiogenic ability of

linomide by androgen ablation involves down-regulation of vascular

endothelial growth factor in human androgen-responsive prostatic cancers."

Cancer Res 57(6): 1054-7.

Jung, J. W., A. R. Ji, et al. (2002). "Organization of the human PTK7 gene encoding a

receptor protein tyrosine kinase-like molecule and alternative splicing of its

mRNA." Biochim Biophys Acta 1579(2-3): 153-63.

Kahn, J., F. Mehraban, et al. (2000). "Gene expression profiling in an in vitro model

of angiogenesis." Am. J. Pathol. 156(6): 1887-900.

Kawakami, Y., T. Fujita, et al. (2004). "Identification of human tumor antigens and its

implications for diagnosis and treatment of cancer." Cancer Sci 95(10): 784-

91.

Kerbel, R. S. (1991). "Inhibition of tumor angiogenesis as a strategy to circumvent

acquired resistance to anti-cancer therapeutic agents." Bioessays 13(1): 31-6.

201

Kim, H., S. W. Nam, et al. (2004). "Different gene expression profiles between

microsatellite instability-high and microsatellite stable colorectal carcinomas."

Oncogene 23(37): 6218-25.

Kim, S. B., S. A. Kang, et al. (1996). "Effects of hypoxia on the extracellular matrix

production of cultured rat mesangial cells." Nephron 72(2): 275-80.

Kivirikko, K. I. and T. Pihlajaniemi (1998). "Collagen hydroxylases and the protein

disulfide isomerase subunit of prolyl 4-hydroxylases." Adv Enzymol Relat

Areas Mol Biol 72: 325-98.

Klose, J. (1975). "Protein mapping by combined isoelectric focusing and

electrophoresis of mouse tissues. A novel approach to testing for induced point

mutations in mammals." Humangenetik 26(3): 231-43.

Kolonin, M., R. Pasqualini, et al. (2001). "Molecular addresses in blood vessels as

targets for therapy." Current Opinion in Chemical Biology 5(3): 308-313.

Koomagi, R. and M. Volm (1998). "Tissue-factor expression in human non-small-cell

lung carcinoma measured by immunohistochemistry: correlation between

tissue factor and angiogenesis." Int J Cancer 79(1): 19-22.

Korte, W. (2000). "Changes of the coagulation and fibrinolysis system in malignancy:

their possible impact on future diagnostic and therapeutic procedures." Clin

Chem Lab Med 38(8): 679-92.

Kratzer, R., C. Eckerskorn, et al. (1998). "Suppression effects in enzymatic peptide

ladder sequencing using ultraviolet - matrix assisted laser

desorption/ionization - mass spectormetry." Electrophoresis 19(11): 1910-9.

Kumar, C. C. (2003). "Integrin alpha v beta 3 as a therapeutic target for blocking

tumor-induced angiogenesis." Curr Drug Targets 4(2): 123-31.

202

Lai, M., E. Sirimanne, et al. (1996). "Sequential patterns of inhibin subunit gene

expression following hypoxic-ischemic injury in the rat brain." Neuroscience

70(4): 1013-24.

Lal, A., H. Peters, et al. (2001). "Transcriptional response to hypoxia in human

tumors." J Natl Cancer Inst 93(17): 1337-43.

Lampugnani, M. G. and E. Dejana (1997). "Interendothelial junctions: structure,

signalling and functional roles." Curr Opin Cell Biol 9(5): 674-82.

Lawson, N. D. and B. M. Weinstein (2002). "Arteries and veins: making a difference

with zebrafish." Nat Rev Genet 3(9): 674-82.

Le Jan, S., C. Amy, et al. (2003). "Angiopoietin-like 4 is a proangiogenic factor

produced during ischemia and in conventional renal cell carcinoma." Am J

Pathol 162(5): 1521-8.

Le, Y. J. and P. M. Corry (1999). "Hypoxia-induced bFGF gene expression is

mediated through the JNK signal transduction pathway." Mol Cell Biochem

202(1-2): 1-8.

LeCouter, J., R. Lin, et al. (2003). "The endocrine-gland-derived VEGF homologue

Bv8 promotes angiogenesis in the testis: Localization of Bv8 receptors to

endothelial cells." Proc Natl Acad Sci U S A 100(5): 2685-90.

Lee, M. H., I. Reynisdottir, et al. (1995). "Cloning of p57KIP2, a cyclin-dependent

kinase inhibitor with unique domain structure and tissue distribution." Genes

Dev. 9(6): 639-49.

Lee, S. Y., A. Madan, et al. (2002). "Lactase gene transcription is activated in

response to hypoxia in intestinal epithelial cells." Mol Genet Metab 75(1): 65-

9.

203

Levinson, H., J. E. Hopper, et al. (2002). "Overexpression of integrin alphav promotes

human osteosarcoma cell populated collagen lattice contraction and cell

migration." J Cell Physiol 193(2): 219-24.

Liang, P. and A. B. Pardee (1992). "Differential display of eukaryotic messenger

RNA by means of the polymerase chain reaction." Science 257(5072): 967-71.

Link, A. J., J. Eng, et al. (1999). "Direct analysis of protein complexes using mass

spectrometry." Nat Biotechnol 17(7): 676-82.

Lipshutz, R. J. (2000). "Applications of high-density oligonucleotide arrays." Novartis

Found. Symp. 229: 84-90; discussion 90-3.

Lipshutz, R. J., S. P. Fodor, et al. (1999). "High density synthetic oligonucleotide

arrays." Nat. Genet. 21(1 Suppl): 20-4.

Liston, P., N. Roy, et al. (1996). "Suppression of apoptosis in mammalian cells by

NAIP and a related family of IAP genes." Nature 379(6563): 349-53.

Liu, B., L. Huang, et al. (2002). "Towards proteome-wide production of monoclonal

antibody by phage display." J. Mol. Biol. 315(5): 1063-73.

Liu, H., P. Moy, et al. (1997). "Monoclonal antibodies to the extracellular domain of

prostate-specific membrane antigen also react with tumor vascular

endothelium." Cancer Res. 57(17): 3629-34.

Lockhart, D. J., H. Dong, et al. (1996). "Expression monitoring by hybridization to

high-density oligonucleotide arrays." Nat Biotechnol 14(13): 1675-80.

Loffing, J., D. Loffing-Cueni, et al. (2001). "Distribution of transcellular calcium and

sodium transport pathways along mouse distal nephron." Am J Physiol Renal

Physiol 281(6): F1021-7.

204

Lopez-Nieto, C. E., G. You, et al. (1997). "Molecular cloning and characterization of

NKT, a gene product related to the organic cation transporter family that is

almost exclusively expressed in the kidney." J Biol Chem 272(10): 6471-8.

Louis, N. A. and L. A. Witters (1992). "Glucose regulation of acetyl-CoA carboxylase

in hepatoma and islet cells." J Biol Chem 267(4): 2287-93.

Luche, S., V. Santoni, et al. (2003). "Evaluation of nonionic and zwitterionic

detergents as membrane protein solubilizers in two-dimensional

electrophoresis." Proteomics 3(3): 249-53.

Lwaleed, B. A., M. Chisholm, et al. (1999). "Urinary tissue factor levels in patients

with breast and colorectal cancer." J Pathol 187(3): 291-4.

Madri, J. A. and S. K. Williams (1983). "Capillary endothelial cell cultures:

phenotypic modulation by matrix components." J Cell Biol 97(1): 153-65.

Maeda, H., J. Fang, et al. (2003). "Vascular permeability enhancement in solid tumor:

various factors, mechanisms involved and its implications." Int

Immunopharmacol 3(3): 319-28.

Magdolen, V., M. Burgle, et al. (2001). "Cyclo19,31[D-Cys19]-uPA19-31 is a potent

competitive antagonist of the interaction of urokinase-type plasminogen

activator with its receptor (CD87)." Biol Chem 382(8): 1197-205.

Magnard, C., R. Bachelier, et al. (2002). "BRCA1 interacts with acetyl-CoA

carboxylase through its tandem of BRCT domains." Oncogene 21(44): 6729-

39.

Mahon, P. C., K. Hirota, et al. (2001). "FIH-1: a novel protein that interacts with HIF-

1alpha and VHL to mediate repression of HIF-1 transcriptional activity."

Genes Dev 15(20): 2675-86.

205

Mailhos, C., U. Modlich, et al. (2001). "Delta4, an endothelial specific notch ligand

expressed at sites of physiological and tumor angiogenesis." Differentiation;

Research In Biological Diversity 69(2-3): 135-144.

Mandriota, S. J., L. Jussila, et al. (2001). "Vascular endothelial growth factor-C-

mediated lymphangiogenesis promotes tumour metastasis." Embo J 20(4):

672-82.

Maniotis, A. J., R. Folberg, et al. (1999). "Vascular channel formation by human

melanoma cells in vivo and in vitro: vasculogenic mimicry." Am J Pathol

155(3): 739-52.

Matsudaira, P. (1987). "Sequence from picomole quantities of proteins electroblotted

onto polyvinylidene difluoride membranes." J Biol Chem 262(21): 10035-8.

Matsuura, H. and S. I. Hakomori (1985). "The oncofetal domain of fibronectin

defined by monoclonal antibody FDC-6: Its presence in fibronectins from fetal

and tumor tissues and its absence in those from normal adult tissues and

plasma." PROC. NATL ACAD. SCI. U. S. A. 82(19): 6517-6521.

Max, R., R. R. C. M. Gerritsen, et al. (1997). "Immunohistochemical analysis of

integrin [alpha]v[beta]3 expression on tumor- associated vessels of human

carcinomas." International Journal of Cancer 71(3): 320-324.

Maxwell, P. H., G. U. Dachs, et al. (1997). "Hypoxia-inducible factor-1 modulates

gene expression in solid tumors and influences both angiogenesis and tumor

growth." Proc Natl Acad Sci U S A 94(15): 8104-9.

McCafferty, J. and D. R. Glover (2000). "Engineering therapeutic proteins." Curr

Opin Struct Biol 10(4): 417-20.

206

McCarthy, M. J., M. Crowther, et al. (1998). "The endothelial receptor tyrosine kinase

tie-1 is upregulated by hypoxia and vascular endothelial growth factor." FEBS

Lett 423(3): 334-8.

McCormack, A. L., D. M. Schieltz, et al. (1997). "Direct analysis and identification of

proteins in mixtures by LC/MS/MS and database searching at the low-

femtomole level." Anal Chem 69(4): 767-76.

McDonald, D. M. and P. Baluk (2002). "Significance of blood vessel leakiness in

cancer." Cancer Res 62(18): 5381-5.

McDonald, D. M., L. Munn, et al. (2000). "Vasculogenic mimicry: how convincing,

how novel, and how significant?" Am J Pathol 156(2): 383-8.

McDonnell, S. and L. M. Matrisian (1990). "Stromelysin in tumor progression and

metastasis." Cancer Metastasis Rev. 9(4): 305-19.

McIntosh, D. P., X. Y. Tan, et al. (2002). "Targeting endothelium and its dynamic

caveolae for tissue-specific transcytosis in vivo: a pathway to overcome cell

barriers to drug and gene delivery." Proc Natl Acad Sci U S A 99(4): 1996-

2001.

Mellergard, P., Y. Ou-Yang, et al. (1994). "Relationship between intra- and

extracellular pH in primary cultures of rat astrocytes." Am. J. Physiol. 267(2

Pt 1): C581-9.

Melton, D. A., P. A. Krieg, et al. (1984). "Efficient in vitro synthesis of biologically

active RNA and RNA hybridization probes from plasmids containing a

bacteriophage SP6 promoter." Nucleic Acids Res 12(18): 7035-56.

Minard, M. E., L. S. Kim, et al. (2004). "The role of the guanine nucleotide exchange

factor Tiam1 in cellular migration, invasion, adhesion and tumor progression."

Breast Cancer Res Treat 84(1): 21-32.

207

Mitchell, P. (2004). "Erbitux diagnostic latest adjunct to cancer therapy." Nat

Biotechnol 22(4): 363-4.

Mitchell, P. (2004). "News In Brief: Avastin approved." Nature Biotechnology 22(4):

371-374.

Molloy, M. P., B. R. Herbert, et al. (1998). "Extraction of membrane proteins by

differential solubilization for separation using two-dimensional gel

electrophoresis." Electrophoresis 19(5): 837-44.

Molloy, M. P., B. R. Herbert, et al. (1999). "Extraction of Escherichia coli proteins

with organic solvents prior to two-dimensional electrophoresis."

Electrophoresis 20(4-5): 701-4.

Monroe, D. M., M. Hoffman, et al. (2002). "Platelets and thrombin generation."

Arteriosclerosis, Thrombosis, and Vascular Biology 22(9): 1381-1389.

Morita, K., M. Furuse, et al. (1999). "Claudin multigene family encoding four-

transmembrane domain protein components of tight junction strands." Proc

Natl Acad Sci U S A 96(2): 511-6.

Muller, A. M., M. I. Hermanns, et al. (2002). "Comparative study of adhesion

molecule expression in cultured human macro- and microvascular endothelial

cells." Exp Mol Pathol 73(3): 171-80.

Muller-Tidow, C., J. Schwable, et al. (2004). "High-throughput analysis of genome-

wide receptor tyrosine kinase expression in human cancers identifies potential

novel drug targets." Clin Cancer Res 10(4): 1241-9.

Muragaki, Y., N. Abe, et al. (1994). "The human alpha 1(XV) collagen chain contains

a large amino-terminal non-triple helical domain with a tandem repeat

structure and homology to alpha 1(XVIII) collagen." J. Biol. Chem. 269(6):

4042-6.

208

Murphy, G. P., G. M. Kenny, et al. (1998). "Measurement of serum prostate-specific

membrane antigen, a new prognostic marker for prostate cancer." Urology

51(5, Supplement 1): 89-97.

Murphy, G. P., S. Su, et al. (2000). "Serum levels of PSMA." Prostate 42(4): 318-319.

Nagy, J. A., L. F. Brown, et al. (1989). "Pathogenesis of tumor stroma generation: a

critical role for leaky blood vessels and fibrin deposition." Biochim Biophys

Acta 948(3): 305-26.

Nakasaki, T., H. Wada, et al. (2002). "Expression of tissue factor and vascular

endothelial growth factor is associated with angiogenesis in colorectal cancer."

Am J Hematol 69(4): 247-54.

Nanus, D. M., M. I. Milowsky, et al. (2003). "Clinical use of monoclonal antibody

HuJ591 therapy: targeting prostate specific membrane antigen." J Urol 170(6

Pt 2): S84-8; discussion S88-9.

Narravula, S. and S. P. Colgan (2001). "Hypoxia-inducible factor 1-mediated

inhibition of peroxisome proliferator-activated receptor alpha expression

during hypoxia." J Immunol 166(12): 7543-8.

Nelson, R. W., M. A. McLean, et al. (1994). "Quantitative determination of proteins

by matrix-assisted laser desorption/ionization time-of-flight mass

spectrometry." Anal Biochem 66: 1408-1415.

Neri, D., B. Carnemolla, et al. (1997). "Targeting by affinity-matured recombinant

antibody fragments of an angiogenesis associated fibronectin isoform." Nat

Biotechnol 15(12): 1271-5.

Newton, G., S. Weremowicz, et al. (1994). "Characterization of human and mouse

cartilage oligomeric matrix protein." Genomics 24(3): 435-9.

209

Nielsen, H. J., H. Pappot, et al. (1998). "Association between plasma concentrations

of plasminogen activator inhibitor-1 and survival in patients with colorectal

cancer." Bmj 316(7134): 829-30.

Niizeki, H., M. Kobayashi, et al. (2002). "Hypoxia enhances the expression of

autocrine motility factor and the motility of human pancreatic cancer cells." Br

J Cancer 86(12): 1914-9.

Nilsson, F., H. Kosmehl, et al. (2001). "Targeted delivery of tissue factor to the ED-B

domain of fibronectin, a marker of angiogenesis, mediates the infarction of

solid tumors in mice." Cancer Res. 61(2): 711-6.

Nishiu, J., T. Tanaka, et al. (1998). "Identification of a novel gene (ECM2) encoding a

putative extracellular matrix protein expressed predominantly in adipose and

female-specific tissues and its chromosomal localization to 9q22.3." Genomics

52(3): 378-81.

Nugent, K. D., W. G. Burton, et al. (1988). "Separation of proteins by reversed-phase

high-performance liquid chromatography. II. Optimizing sample pretreatment

and mobile phase conditions." J Chromatogr 443: 381-97.

Nygren, C., H. von Holst, et al. (1997). "Increased activity of lysosomal

glycohydrolases in glioma tissue and surrounding areas from human brain."

Acta Neurochir (Wien) 139(2): 146-50.

O'Farrell, P. H. (1975). "High resolution two-dimensional electrophoresis of

proteins." J Biol Chem 250(10): 4007-21.

O'Garra, A. and P. Vieira (1992). "Polymerase chain reaction for detection of

cytokine gene expression." Curr Opin Immunol 4(2): 211-5.

Ogawa, S., M. Clauss, et al. (1991). "Hypoxia induces endothelial cell synthesis of

membrane-associated proteins." Proc Natl Acad Sci U S A 88(21): 9897-901.

210

Ogawa, S., H. Gerlach, et al. (1990). "Hypoxia modulates the barrier and coagulant

function of cultured bovine endothelium. Increased monolayer permeability

and induction of procoagulant properties." J Clin Invest 85(4): 1090-8.

Ogita, T., E. Hashimoto, et al. (2001). "Hypoxic induction of adrenomedullin in

cultured human umbilical vein endothelial cells." J Hypertens 19(3 Pt 2): 603-

8.

Oh, P., Y. Li, et al. (2004). "Subtractive proteomic mapping of the endothelial surface

in lung and solid tumours for tissue-specific therapy." Nature 429(6992): 629-

35.

Ohizumi, I., N. Harada, et al. (2000). "Association of CD44 with OTS-8 in tumor

vascular endothelial cells." Biochim. Biophys. Acta 1497(2): 197-203.

Ohizumi, I., S. Tsunoda, et al. (1998). "Identification of tumor vascular antigens by

monoclonal antibodies prepared from rat-tumor-derived endothelial cells." Int.

J. Cancer 77(4): 561-6.

Olsen, H. S., M. A. Cepeda, et al. (1996). "Human stanniocalcin: a possible hormonal

regulator of mineral metabolism." Proc. Natl. Acad. Sci. U. S. A. 93(5): 1792-

6.

O'Reilly, M. S., T. Boehm, et al. (1997). "Endostatin: an endogenous inhibitor of

angiogenesis and tumor growth." Cell 88(2): 277-85.

O'Reilly, M. S., L. Holmgren, et al. (1994). "Angiostatin: a novel angiogenesis

inhibitor that mediates the suppression of metastases by a Lewis lung

carcinoma." Cell 79(2): 315-28.

O'Reilly, M. S., S. Pirie-Shepherd, et al. (1999). "Antiangiogenic activity of the

cleaved conformation of the serpin antithrombin." Science 285(5435): 1926-8.

211

Ostadal, B., F. Kolar, et al. (1995). "Ontogenetic differences in cardiopulmonary

adaptation to chronic hypoxia." Physiol Res 44(1): 45-51.

Padera, T. P., A. Kadambi, et al. (2002). "Lymphatic metastasis in the absence of

functional intratumor lymphatics." Science 296(5574): 1883-6.

Paganelli, G., M. Bartolomei, et al. (2001). "Pre-targeted locoregional

radioimmunotherapy with 90Y-biotin in glioma patients: phase I study and

preliminary therapeutic results." Cancer Biother. Radiopharm. 16(3): 227-35.

Paganelli, G., C. Grana, et al. (1999). "Antibody-guided three-step therapy for high

grade glioma with yttrium-90 biotin." European Journal of Nuclear Medicine

26(4): 348-357.

Park, H., M. A. Adams, et al. (2000). "Hypoxia induces the expression of a 43-kDa

protein (PROXY-1) in normal and malignant cells." Biochem Biophys Res

Commun 276(1): 321-8.

Pasquali, M., M. J. Still, et al. (1997). "Abnormal formation of collagen cross-links in

skin fibroblasts cultured from patients with Ehlers-Danlos syndrome type VI."

Proc Assoc Am Physicians 109(1): 33-41.

Pasqualini, R., E. Koivunen, et al. (2000). "Aminopeptidase N is a receptor for tumor-

homing peptides and a target for inhibiting angiogenesis." Cancer Res 60(3):

722-7.

Pasqualini, R. and E. Ruoslahti (1996). "Organ targeting in vivo using phage display

peptide libraries." Nature 380(6572): 364-6.

Patton, W. F., B. Schulenberg, et al. (2002). "Two-dimensional gel electrophoresis;

better than a poke in the ICAT?" Curr Opin Biotechnol 13(4): 321-8.

212

Paulsen, I. T., M. K. Sliwinski, et al. (1998). "Unified inventory of established and

putative transporters encoded within the complete genome of Saccharomyces

cerevisiae." FEBS Lett 430(1-2): 116-25.

Pecheur, I., O. Peyruchaud, et al. (2002). "Integrin alpha(v)beta3 expression confers

on tumor cells a greater propensity to metastasize to bone." Faseb J 16(10):

1266-8.

Pe'er, J., D. Shweiki, et al. (1995). "Hypoxia-induced expression of vascular

endothelial growth factor by retinal cells is a common factor in

neovascularizing ocular diseases." Lab Invest 72(6): 638-45.

Peirce, M. J., R. Wait, et al. (2004). "Expression profiling of lymphocyte plasma

membrane proteins." Mol Cell Proteomics 3(1): 56-65.

Penta, K., J. A. Varner, et al. (1999). "Del1 induces integrin signaling and

angiogenesis by ligation of alphaVbeta3." J Biol Chem 274(16): 11101-9.

Peters, J. H., J. E. Trevithick, et al. (1995). "Expression of the alternatively spliced

EIIIB segment of fibronectin." Cell Adhes Commun 3(1): 67-89.

Petersen, H. H., M. Hansen, et al. (2001). "Localization of epitopes for monoclonal

antibodies to urokinase-type plasminogen activator: relationship between

epitope localization and effects of antibodies on molecular interactions of the

enzyme." Eur J Biochem 268(16): 4430-9.

Petronini, P. G., R. Alfieri, et al. (1995). "Effect of an alkaline shift on induction of

the heat shock response in human fibroblasts." J. Cell Physiol. 162(3): 322-9.

Pfister, B. E., M. B. Aydelotte, et al. (2001). "Del1: a new protein in the superficial

layer of articular cartilage." Biochem Biophys Res Commun 286(2): 268-73.

213

Phelan, M. W., L. W. Forman, et al. (1998). "Hypoxia increases thrombospondin-1

transcript and protein in cultured endothelial cells." J Lab Clin Med 132(6):

519-29.

Pini, A., F. Viti, et al. (1998). "Design and use of a phage display library. Human

antibodies with subnanomolar affinity against a marker of angiogenesis eluted

from a two-dimensional gel." J. Biol. Chem. 273(34): 21769-76.

Ploug, M., S. Ostergaard, et al. (2001). "Peptide-derived antagonists of the urokinase

receptor. affinity maturation by combinatorial chemistry, identification of

functional epitopes, and inhibitory effect on cancer cell intravasation."

Biochemistry 40(40): 12157-68.

Posey, J. A., M. B. Khazaeli, et al. (2001). "A pilot trial of Vitaxin, a humanized anti-

vitronectin receptor (anti alpha v beta 3) antibody in patients with metastatic

cancer." Cancer Biother Radiopharm 16(2): 125-32.

Prandoni, P., A. Piccioli, et al. (1999). "Cancer and venous thromboembolism: an

overview." Haematologica 84(5): 437-45.

Prediger, E. A. (2001). "Detection and quantitation of mRNAs using ribonuclease

protection assays." Methods Mol Biol 160: 495-505.

Qi, H., P. Juo, et al. (2001). "Caspase-mediated cleavage of the TIAM1 guanine

nucleotide exchange factor during apoptosis." Cell Growth Differ 12(12): 603-

11.

Rabilloud, T. (2002). "Two-dimensional gel electrophoresis in proteomics: old, old

fashioned, but it still climbs up the mountains." Proteomics 2(1): 3-10.

Rabilloud, T., E. Gianazza, et al. (1990). "Amidosulfobetaines, a family of detergents

with improved solubilization properties: application for isoelectric focusing

under denaturing conditions." Anal Biochem 185(1): 94-102.

214

Rafii, S. and M. Skobe (2003). "Splitting vessels: keeping lymph apart from blood."

Nat Med 9(2): 166-8.

Rajotte, D., W. Arap, et al. (1998). "Molecular heterogeneity of the vascular

endothelium revealed by in vivo phage display." J Clin Invest 102(2): 430-7.

Ran, S., A. Downes, et al. (2002). "Increased exposure of anionic phospholipids on

the surface of tumor blood vessels." Cancer Res 62(21): 6132-40.

Ran, S., B. Gao, et al. (1998). "Infarction of solid Hodgkin's tumors in mice by

antibody-directed targeting of tissue factor to tumor vasculature." Cancer Res

58(20): 4646-53.

Ran, S. and P. E. Thorpe (2002). "Phosphatidylserine is a marker of tumor vasculature

and a potential target for cancer imaging and therapy." Int J Radiat Oncol Biol

Phys 54(5): 1479-84.

Rangel, L. B., R. Agarwal, et al. (2003). "Tight junction proteins claudin-3 and

claudin-4 are frequently overexpressed in ovarian cancer but not in ovarian

cystadenomas." Clin Cancer Res 9(7): 2567-75.

Ratcliffe, P. J., J. F. O'Rourke, et al. (1998). "Oxygen sensing, hypoxia-inducible

factor-1 and the regulation of mammalian gene expression." J Exp Biol 201(Pt

8): 1153-62.

Reardon, D. A., G. Akabani, et al. (2002). "Phase II trial of murine (131)I-labeled

antitenascin monoclonal antibody 81C6 administered into surgically created

resection cavities of patients with newly diagnosed malignant gliomas." J.

Clin. Oncol. 20(5): 1389-97.

Richard, D. E., E. Berra, et al. (1999). "Angiogenesis: how a tumor adapts to

hypoxia." Biochem Biophys Res Commun 266(3): 718-22.

215

Rickles, F. R. and A. Falanga (2001). "Molecular basis for the relationship between

thrombosis and cancer." Thromb Res 102(6): V215-24.

Risau, W. (1997). "Mechanisms of angiogenesis." Nature 386(6626): 671-4.

Risau, W. and I. Flamme (1995). "Vasculogenesis." Annu Rev Cell Dev Biol 11: 73-

91.

Riva, P., A. Arista, et al. (1992). "Treatment of intracranial human glioblastoma by

direct intratumoral administration of I-labelled anti-tenascin monoclonal

antibody BC-2." INT. J. CANCER 51(1): 7-13.

Rosenbloom, J., M. Bashir, et al. (1991). "Elastin genes and regulation of their

expression." Crit. Rev. Eukaryot. Gene Expr. 1(3): 145-56.

Rousch, M., J. T. Lutgerink, et al. (1998). "Somatostatin displayed on filamentous

phage as a receptor-specific agonist." British Journal of Pharmacology 125(1):

5-16.

Rubin, J. S., H. Osada, et al. (1989). "Purification and characterization of a newly

identified growth factor specific for epithelial cells." Proc. Natl. Acad. Sci. U.

S. A. 86(3): 802-6.

Ruoslahti, E. (2000). "Targeting tumor vasculature with homing peptides from phage

display." Seminars in Cancer Biology 10(6): 435-442.

Rybak, J.-N., A. Ettorre, et al. (2005). "In vivo protein biotinylation for the

identification of organ-specific antigens accessible from the vasculature."

Nature Meth 2: in press.

Rybak, J. N., S. B. Scheurer, et al. (2004). "Purification of biotinylated proteins on

streptavidin resin: a protocol for quantitative elution." Proteomics 4(8): 2296-

9.

216

Sabarth, N., S. Lamer, et al. (2002). "Identification of surface proteins of Helicobacter

pylori by selective biotinylation, affinity purification, and two-dimensional gel

electrophoresis." J Biol Chem 277(31): 27896-902.

Santimaria, M., G. Moscatelli, et al. (2003). "Immunoscintigraphic detection of the

ED-B domain of fibronectin, a marker of angiogenesis, in patients with

cancer." Clin Cancer Res 9(2): 571-9.

Santoni, V., M. Molloy, et al. (2000). "Membrane proteins and proteomics: un amour

impossible?" Electrophoresis 21(6): 1054-70.

Saransaari, P. and S. S. Oja (1999). "Characteristics of ischemia-induced taurine

release in the developing mouse hippocampus." Neuroscience 94(3): 949-54.

Scarlett, J. D., P. J. Thurlow, et al. (1987). "Plasma-dependent and -independent

mechanisms of platelet aggregation induced by human tumour cell lines."

Thromb Res 46(5): 715-26.

Schaaf, G. J., J. M. Ruijter, et al. (2005). "Full transcriptome analysis of

rhabdomyosarcoma, normal and fetal skeletal muscle: statistical comparison

of multiple SAGE libraries." Faseb J.

Schaffer, S. W., V. Pastukh, et al. (2002). "Effect of ischemia, calcium depletion and

repletion, acidosis and hypoxia on cellular taurine content." Amino Acids

23(4): 395-400.

Schaper, W. and I. Buschmann (1999). "Arteriogenesis, the good and bad of it."

Cardiovasc Res 43(4): 835-7.

Schena, M., D. Shalon, et al. (1995). "Quantitative monitoring of gene expression

patterns with a complementary DNA microarray." Science 270(5235): 467-70.

217

Scheurer, S. B., J.-N. Rybak, et al. (2005). "Identification and relative quantification

of membrane proteins by surface biotinylation and two-dimensional peptide

mapping." Proteomics 5: in press.

Scheurer, S. B., J.-N. Rybak, et al. (2004). Antibody-based vascular targeting:

proteomic techniques for the identification and quantitation of membrane

proteins on endothelial cells. Biomedical Applications of Proteomics. J. C.

Sanchez, D. F. Hochstrasser and G. L. Corthals. Weinheim, WILEY-VCH:

19-38.

Scheurer, S. B., J.-N. Rybak, et al. (2004). "Modulation of gene expression by

hypoxia in human umbilical cord vein endothelial cells. A transcriptomic and

proteomic study." Proteomics 4(6): 1737-1760.

Schioppa, T., B. Uranchimeg, et al. (2003). "Regulation of the Chemokine Receptor

CXCR4 by Hypoxia." J Exp Med 198(9): 1391-1402.

Schnitzer, J. E. (1998). "Vascular targeting as a strategy for cancer therapy." New

England Journal of Medicine 339(7): 472-474.

Schnitzer, J. E., P. Oh, et al. (1995). "Caveolae from luminal plasmalemma of rat lung

endothelium: microdomains enriched in caveolin, Ca(2+)-ATPase, and

inositol trisphosphate receptor." Proceedings Of The National Academy Of

Sciences Of The United States Of America 92(5): 1759-1763.

Schraml, P., J. Kononen, et al. (1999). "Tissue microarrays for gene amplification

surveys in many different tumor types." Clin Cancer Res 5(8): 1966-75.

Schulz-Knappe, P., H. D. Zucht, et al. (2001). "Peptidomics: the comprehensive

analysis of peptides in complex biological mixtures." Comb Chem High

Throughput Screen 4(2): 207-17.

218

Semenza, G. (2002). "Signal transduction to hypoxia-inducible factor 1." Biochem

Pharmacol 64(5-6): 993-8.

Semenza, G. L. (1999). "Regulation of mammalian O2 homeostasis by hypoxia-

inducible factor 1." Annu Rev Cell Dev Biol 15: 551-78.

Semenza, G. L. (2001). "HIF-1, O(2), and the 3 PHDs: how animal cells signal

hypoxia to the nucleus." Cell 107(1): 1-3.

Semenza, G. L. (2002). "HIF-1 and tumor progression: pathophysiology and

therapeutics." Trends Mol Med 8(4 Suppl): S62-7.

Semenza, G. L., B. H. Jiang, et al. (1996). "Hypoxia response elements in the aldolase

A, enolase 1, and lactate dehydrogenase A gene promoters contain essential

binding sites for hypoxia-inducible factor 1." J Biol Chem 271(51): 32529-37.

Semenza, G. L., M. K. Nejfelt, et al. (1991). "Hypoxia-inducible nuclear factors bind

to an enhancer element located 3' to the human erythropoietin gene." Proc Natl

Acad Sci U S A 88(13): 5680-4.

Seta, K. A., R. Kim, et al. (2001). "Hypoxia-induced regulation of MAPK

phosphatase-1 as identified by subtractive suppression hybridization and

cDNA microarray analysis." J Biol Chem 276(48): 44405-12.

Sharov, V. S., N. A. Galeva, et al. (2002). "Two-dimensional separation of the

membrane protein sarcoplasmic reticulum Ca-ATPase for high-performance

liquid chromatography-tandem mass spectrometry analysis of posttranslational

protein modifications." Anal Biochem 308(2): 328-35.

Shi, Q., X. Le, et al. (2001). "Regulation of vascular endothelial growth factor

expression by acidosis in human cancer cells." Oncogene 20(28): 3751-6.

Shigemori, C., H. Wada, et al. (1998). "Tissue factor expression and metastatic

potential of colorectal cancer." Thromb Haemost 80(6): 894-8.

219

Siemeister, G., M. Schirner, et al. (1999). "Two independent mechanisms essential for

tumor angiogenesis: inhibition of human melanoma xenograft growth by

interfering with either the vascular endothelial growth factor receptor pathway

or the Tie-2 pathway." Cancer Res 59(13): 3185-91.

Silacci, M., S. Brack, et al. (2005). "Design, construction and characterization of a

large synthetic human antibody phage display library." Proteomics 5: in press.

Simpson, R. J., L. M. Connolly, et al. (2000). "Proteomic analysis of the human colon

carcinoma cell line (LIM 1215): development of a membrane protein

database." Electrophoresis 21(9): 1707-32.

Sipkins, D. A., D. A. Cheresh, et al. (1998). "Detection of tumor angiogenesis in vivo

by alphaVbeta3-targeted magnetic resonance imaging." Nat Med 4(5): 623-6.

Smolka, M., H. Zhou, et al. (2002). "Quantitative protein profiling using two-

dimensional gel electrophoresis, isotope-coded affinity tag labeling, and mass

spectrometry." Mol Cell Proteomics 1(1): 19-29.

Sorokin, P. (2000). "Mylotarg approved for patients with CD33+ acute myeloid

leukemia." Clin J Oncol Nurs 4(6): 279-80.

Speers, A. E. and B. F. Cravatt (2004). "Profiling enzyme activities in vivo using

click chemistry methods." Chem Biol 11(4): 535-46.

Sriuranpong, V., A. Mutirangura, et al. (2004). "Global gene expression profile of

nasopharyngeal carcinoma by laser capture microdissection and

complementary DNA microarrays." Clin Cancer Res 10(15): 4944-58.

St. Croix, B., C. Rago, et al. (2000). "Genes expressed in human tumor endothelium."

Science 289(5482): 1197-1202.

Stacker, S. A., M. G. Achen, et al. (2002). "Lymphangiogenesis and cancer

metastasis." Nat Rev Cancer 2(8): 573-83.

220

Staller, P., J. Sulitkova, et al. (2003). "Chemokine receptor CXCR4 downregulated by

von Hippel-Lindau tumour suppressor pVHL." Nature 425(6955): 307-11.

Stevens, T. J. and I. T. Arkin (2000). "Do more complex organisms have a greater

proportion of membrane proteins in their genomes?" Proteins 39(4): 417-20.

Stow, J. L. (2004). "ICAT is a multipotent inhibitor of beta-catenin. Focus on "role

for ICAT in beta-catenin-dependent nuclear signaling and cadherin

functions"." Am J Physiol Cell Physiol 286(4): C745-6.

Stubbs, M., P. M. McSheehy, et al. (2000). "Causes and consequences of tumour

acidity and implications for treatment." Mol. Med. Today 6(1): 15-9.

Takahashi, Y., S. Takahashi, et al. (2000). "Hypoxic induction of prolyl 4-

hydroxylase alpha (I) in cultured cells." J Biol Chem 275(19): 14139-46.

Tammen, H., R. Hess, et al. (2002). "Detection of low-molecular-mass plasma

peptides in the cavernous and systemic blood of healthy men during penile

flaccidity and rigidity--an experimental approach using the novel differential

peptide display technology." Urology 59(5): 784-9.

Tanaka, K., K. Iyama, et al. (1997). "Differential expression of alpha 1(IV), alpha

2(IV), alpha 5(IV) and alpha 6(IV) collagen chains in the basement membrane

of basal cell carcinoma." Histochem J 29(7): 563-70.

Tannock, I. F. (1968). "The relation between cell proliferation and the vascular

system in a transplanted mouse mammary tumour." Br J Cancer 22(2): 258-73.

Tarli, L., E. Balza, et al. (1999). "A high-affinity human antibody that targets tumoral

blood vessels." Blood 94(1): 192-8.

Taylor, C. T. and S. P. Colgan (1999). "Therapeutic targets for hypoxia-elicited

pathways." Pharm Res 16(10): 1498-505.

221

Tenan, M., G. Fulci, et al. (2000). "Thrombospondin-1 is downregulated by anoxia

and suppresses tumorigenicity of human glioblastoma cells." J Exp Med

191(10): 1789-98.

Thampy, K. G. (1989). "Formation of malonyl coenzyme A in rat heart. Identification

and purification of an isozyme of A carboxylase from rat heart." J Biol Chem

264(30): 17631-4.

Thomas, T. C. and M. G. McNamee (1990). "Purification of membrane proteins."

Methods Enzymol 182: 499-520.

Thompson, A., J. Schafer, et al. (2003). "Tandem mass tags: a novel quantification

strategy for comparative analysis of complex protein mixtures by MS/MS."

Anal Chem 75(8): 1895-904.

Thomson, R. B. and P. S. Aronson (1999). "Immunolocalization of Ksp-cadherin in

the adult and developing rabbit kidney." Am J Physiol 277(1 Pt 2): F146-56.

Thomson, R. B., P. Igarashi, et al. (1995). "Isolation and cDNA cloning of Ksp-

cadherin, a novel kidney-specific member of the cadherin multigene family." J

Biol Chem 270(29): 17594-601.

Thorpe, P. E. (2004). "Vascular targeting agents as cancer therapeutics." Clin Cancer

Res 10(2): 415-27.

Ton, C., D. Stamatiou, et al. (2002). "Construction of a zebrafish cDNA microarray:

gene expression profiling of the zebrafish during development." Biochem

Biophys Res Commun 296(5): 1134-42.

Tsukita, S., M. Furuse, et al. (2001). "Multifunctional strands in tight junctions." Nat

Rev Mol Cell Biol 2(4): 285-93.

222

Tsunoda, S., I. Ohizumi, et al. (1999). "Specific binding of TES-23 antibody to

tumour vascular endothelium in mice, rats and human cancer tissue: a novel

drug carrier for cancer targeting therapy." Br. J. Cancer 81(7): 1155-61.

Tucker, G. C. (2003). "Alpha v integrin inhibitors and cancer therapy." Curr Opin

Investig Drugs 4(6): 722-31.

Tucker, R. P. (2004). "The thrombospondin type 1 repeat superfamily." Int J Biochem

Cell Biol 36(6): 969-74.

Ueda, C., Y. Hirohata, et al. (2001). "Pancreatic cancer complicated by disseminated

intravascular coagulation associated with production of tissue factor." J

Gastroenterol 36(12): 848-50.

Uitto, J., A. M. Christiano, et al. (1991). "Molecular biology and pathology of human

elastin." Biochem. Soc. Trans. 19(4): 824-9.

Vaupel, P., F. Kallinowski, et al. (1989). "Blood flow, oxygen and nutrient supply,

and metabolic microenvironment of human tumors: a review." Cancer Res

49(23): 6449-65.

Vaupel, P., P. Okunieff, et al. (1989). "Blood flow, tissue oxygenation, pH

distribution, and energy metabolism of murine mammary adenocarcinomas

during growth." Adv Exp Med Biol 248: 835-45.

Veikkola, T., M. Lohela, et al. (2003). "Intrinsic versus microenvironmental

regulation of lymphatic endothelial cell phenotype and function." Faseb J

17(14): 2006-13.

Velculescu, V. E., L. Zhang, et al. (1995). "Serial analysis of gene expression."

Science 270(5235): 484-7.

Viti, F., M. A. Bumke, et al. (2000). "A strategy for the Identification of Differentially

Expressed Proteins Secreted by Fibroblasts." Chimia 54(11): 678-682.

223

Viti, F., F. Nilsson, et al. (2000). "Design and use of phage display libraries for the

selection of antibodies and enzymes." Methods Enzymol 326: 480-505.

Viti, F., L. Tarli, et al. (1999). "Increased binding affinity and valence of recombinant

antibody fragments lead to improved targeting of tumoral angiogenesis."

Cancer Res. 59(2): 347-52.

Vrana, J. A., M. T. Stang, et al. (1996). "Expression of tissue factor in tumor stroma

correlates with progression to invasive human breast cancer: paracrine

regulation by carcinoma cell-derived members of the transforming growth

factor beta family." Cancer Res 56(21): 5063-70.

Wakai, Y., J. Matsui, et al. (2000). "Effective cancer targeting using an anti-tumor

tissue vascular endothelium-specific monoclonal antibody (TES-23)."

Japanese Journal of Cancer Research 91(12): 1319-1325.

Wall, D. B., S. J. Berger, et al. (2002). "Continuous sample deposition from reversed-

phase liquid chromatography to tracks on a matrix-assisted laser

desorption/ionization precoated target for the analysis of protein digests."

Electrophoresis 23(18): 3193-204.

Wallin, E. and G. von Heijne (1998). "Genome-wide analysis of integral membrane

proteins from eubacterial, archaean, and eukaryotic organisms." Protein Sci

7(4): 1029-38.

Walter, P. and G. Blobel (1983). "Preparation of microsomal membranes for

cotranslational protein translocation." Methods Enzymol 96: 84-93.

Wang, J. M., S. Kumar, et al. (1993). "A monoclonal antibody detects heterogeneity

in vascular endothelium of tumours and normal tissues." Int J Cancer 54(3):

363-70.

224

Wang, K. J., R. T. Wang, et al. (2004). "Identification of tumor markers using two-

dimensional electrophoresis in gastric carcinoma." World J Gastroenterol

10(15): 2179-83.

Wang, L., M. Xiong, et al. (2000). "The effect of hypoxia on expression of basic

fibroblast growth factor in pulmonary vascular pericytes." J Tongji Med Univ

20(4): 265-7.

Wary, K. K., G. D. Thakker, et al. (2003). "Analysis of VEGF-responsive Genes

Involved in the activation of endothelial cells." Mol Cancer 2(1): 25.

Washburn, M. P., D. Wolters, et al. (2001). "Large-scale analysis of the yeast

proteome by multidimensional protein identification technology." Nat

Biotechnol 19(3): 242-7.

Welling, G. W., R. van der Zee, et al. (1987). "Column liquid chromatography of

integral membrane proteins." J Chromatogr 418: 223-43.

Wewer, U. M. and R. Albrechtsen (1992). "Tetranectin, a plasminogen kringle 4-

binding protein. Cloning and gene expression pattern in human colon cancer."

Lab Invest 67(2): 253-62.

Wewer, U. M., K. Ibaraki, et al. (1994). "A potential role for tetranectin in

mineralization during osteogenesis." J. Cell Biol. 127(6 Pt 1): 1767-75.

Wilhelm, S. M., I. E. Collier, et al. (1987). "Human skin fibroblast stromelysin:

structure, glycosylation, substrate specificity, and differential expression in

normal and tumorigenic cells." Proc. Natl. Acad. Sci. U. S. A. 84(19): 6725-9.

Winter, G., A. D. Griffiths, et al. (1994). "Making antibodies by phage display

technology." Annu Rev Immunol 12: 433-55.

Wojtukiewicz, M. Z., L. R. Zacharski, et al. (1991). "Fibrin formation on vessel walls

in hyperplastic and malignant prostate tissue." Cancer 67(5): 1377-83.

225

Wojtukiewicz, M. Z., L. R. Zacharski, et al. (1989). "Absence of components of

coagulation and fibrinolysis pathways in situ in mesothelioma." Thromb Res

55(2): 279-84.

Wojtukiewicz, M. Z., L. R. Zacharski, et al. (1989). "Indirect activation of blood

coagulation in colon cancer." Thromb Haemost 62(4): 1062-6.

Wolf, M., S. Mousses, et al. (2004). "High-resolution analysis of gene copy number

alterations in human prostate cancer using CGH on cDNA microarrays: impact

of copy number on gene expression." Neoplasia 6(3): 240-7.

Wood, S. M., M. S. Wiesener, et al. (1998). "Selection and analysis of a mutant cell

line defective in the hypoxia-inducible factor-1 alpha-subunit (HIF-1alpha).

Characterization of hif-1alpha-dependent and -independent hypoxia-inducible

gene expression." J Biol Chem 273(14): 8360-8.

Wu, C. C., M. J. MacCoss, et al. (2003). "A method for the comprehensive proteomic

analysis of membrane proteins." Nat Biotechnol 21(5): 532-8.

Wyder, L., A. Vitaliti, et al. (2000). "Increased expression of H/T-cadherin in tumor-

penetrating blood vessels." Cancer Res 60(17): 4682-8.

Yamashita, J. K. (2004). "Differentiation and diversification of vascular cells from

embryonic stem cells." Int J Hematol 80(1): 1-6.

Yang, J., B. C. Furie, et al. (1999). "The biology of P-selectin glycoprotein ligand-1:

its role as a selectin counterreceptor in leukocyte-endothelial and leukocyte-

platelet interaction." Thromb Haemost 81(1): 1-7.

Yano, K., L. F. Brown, et al. (2001). "Control of hair growth and follicle size by

VEGF-mediated angiogenesis." J Clin Invest 107(4): 409-17.

226

Yano, K., L. F. Brown, et al. (2003). "Thrombospondin-1 plays a critical role in the

induction of hair follicle involution and vascular regression during the catagen

phase." J Invest Dermatol 120(1): 14-9.

Yates, J. R., 3rd, S. Speicher, et al. (1993). "Peptide mass maps: a highly informative

approach to protein identification." Anal Biochem 214(2): 397-408.

Yeakley, J. M., J. B. Fan, et al. (2002). "Profiling alternative splicing on fiber-optic

arrays." Nat. Biotechnol. 20(4): 353-8.

Yoon, J. C., T. W. Chickering, et al. (2000). "Peroxisome proliferator-activated

receptor gamma target gene encoding a novel angiopoietin-related protein

associated with adipose differentiation." Mol Cell Biol 20(14): 5343-9.

Zachowski, A., J.-P. Henry, et al. (1989). "Control of transmembrane lipid asymmetry

in chromaffin granules by an ATP-dependent protein." Nature 340(6228): 75-

76.

Zardi, L., B. Carnemolla, et al. (1987). "Transformed human cells produce a new

fibronectin isoform by preferential alternative splicing of a previously

unobserved exon." Embo J. 6(8): 2337-42.

Zhang, H., J. Wada, et al. (2001). "Collectrin, a collecting duct-specific

transmembrane glycoprotein, is a novel homolog of ACE2 and is

developmentally regulated in embryonic kidneys." J Biol Chem 276(20):

17132-9.

Zhang, H., W. Yan, et al. (2004). "Chemical probes and tandem mass spectrometry: a

strategy for the quantitative analysis of proteomes and subproteomes." Curr

Opin Chem Biol 8(1): 66-75.

Zhao, Y., W. Zhang, et al. (2004). "Proteomic analysis of integral plasma membrane

proteins." Anal Chem 76(7): 1817-23.

227

Zhao, Z., C. C. Lee, et al. (1995). "The gene for a human microfibril-associated

glycoprotein is commonly deleted in Smith-Magenis syndrome patients."

Hum. Mol. Genet. 4(4): 589-97.

Zhou, H., J. A. Ranish, et al. (2002). "Quantitative proteome analysis by solid-phase

isotope tagging and mass spectrometry." Nat Biotechnol 20(5): 512-5.

Zhou, H., J. D. Watts, et al. (2001). "A systematic approach to the analysis of protein

phosphorylation." Nat Biotechnol 19(4): 375-8.

228

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