REACTIVE OXYGEN SPECIES, HEPATITIS AND ...ANUIES grant M02-S01, CONACyT grant 39525-M, COST B35 and...

Nº D’ORDRE : 896 .

THESE présentée en vue de l’obtention de grade de Docteur de l’Université de Toulouse délivré par l’Institut National des Sciences

Appliquées de Toulouse

Spécialité : Ingénieries microbienne et enzymatique

Par Mlle. Adriana MARQUEZ-QUIÑONES INTITULE :

REACTIVE OXYGEN SPECIES, HEPATITIS AND CARCINOGENESIS INITIATION: AN INTEGRATIVE APPROACH COMBINING TRANSCRIPTOMIC AND

METABONOMIC PROFILINGS

Espèces réactives de l’oxygène, hépatite et initiation du cancer : une approche intégrée qui combine le profilage transcriptomique et métabonomique.

DATE DE SOUTENANCE : 21 NOVEMBRE 2007 JURY : FONCTION M. CORPET Denis Président M. FRANÇOIS Jean-Marie Directeur de thèse Mme. GUERAUD Françoise Codirectrice de thèse M. EZAN Eric Rapporteur M. GRUNE Tilman Rapporteur M. Del RIO-GUERRA Gabriel Rapporteur M. PARIS Alain Examinateur M. VILLA-TREVIÑO Saúl Examinateur ECOLE DOCTORALE : Sciences Ecologiques, Vétérinaires, Agronomiques et

Bioingénieries (SEVAB) LABORATOIRES : - UMR 5504 & 792-CNRS-INSA-INRA Ingénierie des Systèmes

Biologiques et des Procédés. - UMR 1089-Xénobiotiques INRA-ENVT

3

This work was part of a collaboration between France and Mexico. The work presented here was done in Toulouse France at both the UMR-1089 Xénobiotiques, INRA and at the UMR 5504 & 792 d’Ingénierie des Systèmes Biologiques et Procédés, CNRS-INRA-INSA, and in Mexico City at the laboratory number 50 of the Departamento de Biologia Celular del Centro de Investigación y de Estudios Avanzados del IPN (CINVESTAV). This thesis was directed by Pr. Jean-Marie François and Dr. Françoise Guéraud and advised by Dr. Saúl Villa-Treviño. This work was supported by the Program Alβan, the European Union Program of High Level Scholarships for Latin America, scholarship No. E04D03532MX, ECOS-ANUIES grant M02-S01, CONACyT grant 39525-M, COST B35 and fellowship from CONACyT AMQ165505.

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To my family: my mother, Sues, Rafa, to Syndy, Josué, Sofi To my grandmother… We are the champions! To my grandfather… I miss you To my father… I got it!!! We have found that where science has progressed the furthest, the mind has regained

from nature that which mind has put into nature. We have found a strange footprint on

the shores of the unknown. We have devised profound theories, one after another, to

account for its origin. At last, we have succeeded in reconstructing the creature that

made the footprint. And lo!... It is our own

Sir Arthur Eddington

In What the Bleep do we know?

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ACKNOWLEDGMENTS I would like to express my gratitude to my supervisor, Dr. Françoise Guéraud, whose expertise, patience and friendship added considerably to my graduate experience and to my personal life. I am also extremely grateful to Prof. Jean-Marie François and Dr. Saul Villa-Treviño for the direction of this thesis. Special thanks to Dr. Alain Paris for all the indispensable notes and remarks in the building and development of this ambitious project. I appreciate his vast knowledge in many areas (biology, statistics, physiology, etc) and his patience to share a little bit of it with me. I want to thank Dr. Eric Ezan, Research director at CEA, Saclay-France; Prof. Tilman Grune, professor at the University of Hohenheim, Stuttgart-Germany; and Dr. Gabriel del Rio-Guerra, professor at the Universidad Nacional Autónoma de México, UNAM-Mexico for having accepted of being my external reviewers. Thanks also to Prof. Denis Corpet for accepting to be part of my thesis commitee. Thanks to Balbine Roussel for the valorous work in data analysis and statistics, especially for her friendship and her excellent mood that made my stay in France a really nice time. Especially I want to thank Dr. Julio Pérez-Carreón for being a very good friend and an excellent collaborator and for all the ideas and debates that we share in the development of this work (You would be an excellent psychologist!!!). Thanks to Dr. Neven Žarkovic and Dr. Kamelija Žarkovic for giving us the monoclonal antibody anti-HNE-his, and for their collaboration in histological analyses and interpretation. Thanks also to Ana Čipac for her helping in immunohistochemistry of HNE-his. I am also grateful to all the people of the laboratory for oxidative stress at the Rudjer Boskovic Institute; Zagreb, Croatia, for their welcome in my stay in Zagreb. Thanks to Dr. Jean-Pierre Cravedi, Director of the UMR-1089 Xénobiotiques for receiving me at the laboratory. This thesis would not be possible without all the technical assistance: thanks to Samia Fattel-Fazenda for her help in histological methodologies; to Dr. Cécile Canlet for NMR analyses. Thanks to Céline Domange and Julie Labat for technical helping in liver extraction and sample preparation for NMR analyses. I am also grateful to the Biopuce plateform staff (Dr.Véronique Le Berre, Dr. Serguei Sokol, Lidwine Trouilh, and Delphine Labourdette) for their assistance in transcriptomic work. Thanks to Florence Blas-y-Estrada and Raymond Gazel for animal care. Thanks to Elisabeth Perdu and Dr. Patrick Rouimi for their assistance in biochemical procedures. I am very grateful to Pascal Martin for his remarks and help in data treatment and statistics. I would like to thank all the people of the UMR-1089 INRA/ENVT for their welcome during my stay in France, where I met very good friends and excellent people that I will remember with a lot of happiness. Thanks to: Thierry (The best french teacher I could ever have!), Fabrice, Sylviane, Denis, Nathalie Naud, Nathalie Priymenko, Julian, Maysaloun, Céline Dargent, Céline Domange, Maryse, Jerôme, Delphine, Emilien, Anne, Haïfaa, Chary, François, Suzette, Marie-Hélène, Cécile, Patrick, Laurence and all the others. Finally, I would like to thank my family and my friends for encouraging me to make my PhD, even when we were far away for a while.

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PUBLICATIONS:

• Marquez-Quiñones A, Paris A, Roussel B, Perez-Carreon J, LeBerre V, François JM, Villa-Treviño S, Guéraud F. Proteasome activity deregulation in LEC rat hepatitis: following the insights of transcriptomic analysis. OMICS: A Journal of Integrative Biology. In press

• Marquez A, Villa-Treviño S, Guéraud F. The LEC rat: a useful model for

studying liver carcinogenesis related to oxidative stress and inflammation. Redox Report 2007;12(1):35-9

• García-Roman R, Pérez-Carreón JI, Márquez-Quiñones A, Salcido-Neyoy ME,

Villa-Treviño S. Persistent activation of NF-kappaB related to IkappaB's degradation profiles during early chemical hepatocarcinogenesis. J Carcinogenesis 2007; 6:5

COMMUNICATIONS:

• HNE-Club Meeting; Genoa, Italy (June16/18, 2006); oral communication: “Correlation between oxidative stress and tumor marker expression in the onset of hepatocarcinogenesis in LEC rat”.

• International Free radical Summer School 2006: Biomarkers of oxidative stress, Spetses Island, Greece (September 30-October 6, 2006); poster presentation: “Gene expression profiles from oxidative stress-related hepatocarcinogenesis model: The LEC rat model”. With this presentation we have won the poster award.

• Rudjer Boskovic Institute; Zagreb, Croatia (November 16, 2006); invited oral communication: “Reactive Oxygen Species and Carcinogenesis: An integrative approach combining genetic and metabonomic profiles”.

• Deuxièmes journées scientifiques du Réseau Français de Métabonomique et Fluxomique, Saint-Sauves d'Auvergne, France (13-15 décembre 2006); poster presentation: “The LEC rat hepatocarcinogenesis model: Relationship between transcriptomic and metabonomic signatures”.

• SFRR-europe 2007 Meeting; Vilamoura, Portugal (October 10-13, 2007); oral communication in the “Young Investigator Session”: “Proteasome activity deregulation in LEC rat hepatitis: following the insights of transcriptomic analysis”.

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ABBREVIATIONS: 1H NMR Proton nuclear magnetic resonance

8-IsoPGF2α 8-Isoprostaglandine F2α

8-OHG 8-Hydroxyguanine

ALT Alanine aminotransferase

ARE Antioxidant responsive element

AST Aspartate aminotransferase

BHT Butylhydroxytoluene

CAT Catalase

ChT Chymotrypsin-like

Cu-Mt Copper-metallothionein complexes

DHN-MA 1,4-dihydroxynonane-mercapturic acid

FDA Factorial discriminant analysis

G6Pase Glucose-6-phosphatase

GGT γ-glutamyltranspeptidase GO Gene ontology

GPX Glutathione peroxidase

GSH Reduced glutathione

GSSG Oxidized glutathione

GST Glutathione S-transferase

GSTP Glutathione S-transferase type pi

HBC Hepatitis B virus

HCC Hepatocellular carcinoma

HCV Hepatitis C virus

HNE 4-Hydroxy-2-nonenal

LE Long Evans

LEC Long Evans Cinnamon-like color

MDR Multi-drug resistant protein

NRS Nitrogen reactive species

OH· Hydroxyl radical

OSC Orthogonal signal correction

PGPH Peptidylglutamyl peptide hydrolase

PLS Partial least square

PLS-DA Partial least square-Discriminant analysis

qRT-PCR Quantitative RT-PCR

ROS Reactive oxygen species

SAGE Serial analysis of gene expression

SOD Superoxide dismutase

t-bil Total bilirubin

TL Trypsin-like

VIP Variable Importance Projection

WND Wilson's protein

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FIGURE LIST Figure 1 Sources of cell ROS production 25 Figure 2 Lipid peroxidation process 28 Figure 3 Clinico-pathological description of LEC rat spontaneous hepatitis

and hepatocarcinogenesis 42

Figure 4 Histological and biochemical features of LEC rats at different hepatitis stages

72

Figure 5 Tumor marker expression in liver of LEC rats at different hepatitis stages

74

Figure 6 HNE-protein adduction in LEC rat liver at different hepatitis stages

76

Figure 7 Lipid peroxidation products excretion in urine of LEC rats at different hepatitis stages

77

Figure 8 Diet consumption at different stages of hepatitis in LEC rats 78 Figure 9 Effect of diet consumption on lipid peroxidation products’ urinary

excretion 78

Figure 10 Effect of D-penicillamine on hepatitis development, carcinogenesis initiation and oxidative stress

80

Figure 11 Histological and biochemical parameters comparison between LEC rats groups (Normal 6w and D-penicillamine) and Long Evans rats

82

Figure 12 Ratio/Intensity scatter plots 92 Figure 13 Relation between hepatitis progression and gene expression 93 Figure 14 Factorial Discriminant Analysis (FDA) of transcriptomic data 95 Figure 15 Confirmation of the differential expression of GSTP by

quantitative RT-PCR 96

Figure 16 Gene ontology classification of 59 known genes 97 Figure 17 Metabolism ontology sub-classification 98 Figure 18 Proteasome structure 101 Figure 19 Heat map of proteasome related genes in the liver of LEC rats at

different disease stages 102

Figure 20 Changes in proteasome activities in the liver of LEC rats at different disease stages

104

Figure 21 Immunodetection of proteasome in liver of LEC rats at different disease stages and treatments

105

Figure 22 Effect of D-penicillamine on gene expression patterns 106 Figure 23 FDA of 1H NMR spectra of urine samples 117

Figure 24 PLS-2 regression between 1H NMR metabolic profiles (t[1] axis) and plasma levels of hepatitis markers (u[1] axis)

118

Figure 25 Pattern recognition analysis of 1H NMR spectra of lipophilic extracts from liver samples of LEC rats at different hepatitis stages

120

Figure 26 Pattern recognition analysis of 1H NMR spectra of aqueous extracts from liver samples of LEC rats at different hepatitis stages

122

Figure 27 PLS-2 regression between transcriptomic and metabonomic data 125 Figure S1 1D 1H NMR spectra of liver aqueous fractions 171

Figure S2 1D 1H NMR spectra of liver aqueous fractions 172

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TABLE LIST Table I Principal ROS studied in biological systems 24 Table II Array hybridization design 90 Table III ∆2 Mahalanobis distance between all different groups

represented in FDA test 95

Table IV Differential genes classified in GO: Protein Metabolism 99 Table V Differential genes classified in GO: Nucleobase, Nucleotide

and Nucleic acid Metabolism 100

Table VI Identified metabolites that were related to hepatitis development in LEC rats

118

Table VII ∆2 Mahalanobis distance between groups from FDA analysis of 1H NMR spectra of liver lipophilic extracts

120

Table VIII

∆2 Mahalanobis distance between groups from FDA analysis of 1H NMR spectra of liver aqueous extracts

122

Table IX Discriminant variables selected by ANOVA (P < 0.05) in a FDA

123

Table A List of differential genes that participate in PLS-DA axis construction

153

Table B Correlation between transcriptomic and metabonomic data 167

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PROLOGUE 19 A. INTRODUCTION 21

A.1 THE CONCEPT OF OXIDATIVE STRESS 23

A.1.1 Oxidative DNA damage 26

A.1.2 Lipid peroxidation 27

A.1.3 Protein modification by ROS 29

A.1.4 Cellular adaptive responses to oxidative stress 30

A.2. OXIDATIVE STRESS AND LIVER DISEASES 32

A.2.1 Wilson’s disease 32

A.2.2 Hepatocarcinogenesis 33

A.3 OMICS TECHNOLOGIES AND SYSTEMS BIOLOGY 35

A.3.1 Omics approaches, what do they mean? 35

A.3.2 Transcriptomics 36

A.3.3 Proteomics 37

A.3.4 Metabonomics 38

A.3.5 Systems biology: Integrating the puzzle 38

A.4 THE LEC RAT MODEL 40

A.4.1 Copper accumulation in LEC rats 40

A.4.2 Clinicopathological characteristics of LEC rats 41

A.4.3 Mechanism of hepatocarcinogenesis in LEC rats 43

B. AIMS OF THE STUDY 47

C. MATERIAL AND METHODS 53

C.1 ANIMALS AND TREATMENTS 55

C.2 URINE COLLECTION AND STORING 55

C.3 TISSUE PREPARATION 55

C.4 PLASMA HEPATITIS MARKERS DETERMINATION 56

C.5 OXIDATIVE STRESS DETERMINATION 56

C.5.1 8-Isoprostaglandine F2α (8-IsoPGF2α) urine determination by Enzyme

Immunoassay (EIA) 56 C.5.2 1,4-dihydroxynonane-mercapturic acid (DHN-MA) urine determination by

Enzyme Immunoassay (EIA) 57

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C.6 HISTOLOGY ANALYSES 57

C.6.1 Hematoxylin and eosin staining 57

C.6.2 Immunohistochemistry of Glutathione S-transferase, placental

form (GSTP) 58

C.6.3 Immunohistochemistry of 4-hydroxynonenal (HNE) protein adducts 58

C.6.4 Histological detection of gamma-glutamyl transpeptidase (GGT)

positive hepatocytes 59 C.7 PROTEASOME PEPTIDASE ACTIVITIES 59

C.7.1 Cytosol extraction 59

C.7.2 Proteasome activity assay 60

C.8 SDS-PAGE AND IMMUNOBLOTTING 60

C.9 RNA EXTRACTION 61

C.10 QUANTITATIVE RT-PCR (QRT-PCR) 61

C.11 MICROARRAY ANALYSIS 61

C.11.1 DNA microarray slides 61

C.11.2 Preparation of labeled cDNA 62

C.11.3 Array hybridization and scanning 62

C.11.4 Data analysis and public access 63

C.12. METABONOMIC ANALYSES 63

C.12.1 Samples preparation 63

C.12.2 1H NMR spectroscopic analysis 64

C.12.3 Spectra analysis 65

C.13 STATISTICAL ANALYSES 65

D. RESULTS AND DISCUSION 67

D.1 CHAPTER 1: 69

CLINICAL AND BIOCHEMICAL CHARACTERISATION OF LEC RAT

MODEL

D.1.1 INTRODUCTION AND AIMS 71

D.1.2 EXPERIMENTAL PROTOCOL 71

D.1.3 RESULTS 73

D.1.3.1 Hepatitis development in LEC rats 73

D.1.3.2 GGT and GSTP expression in LEC rat hepatocytes 75

D.1.3.3 Oxidative stress induction in LEC rats 75

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D.1.3.4 Effect of D-penicillamine on hepatitis development 79

D.1.3.5 Comparison between D-penicillamine-treated and normal rats 81

D.1.4 DISCUSSION 81

D.2 CHAPTER 2: 87

TRANSCRIPTOMIC ANALYSIS OF LEC RAT HEPATITIS DEVELOPMENT



D.2.3 RESULTS 91

D.2.3.1 Microarray analysis and validation 91

D.2.3.2 Functional classification of VIP genes 96

D.2.3.3 Proteasome activity 101

D.2.3.4 Effect of D-penicillamine on gene expression 103

D.2.4 DISCUSSION 106

D.3 CHAPTER 3 113

METABONOMIC ANALYSIS AND ITS RELATION WITH

TRANSCRIPTOMIC DATA



D.3.3 RESULTS 116

D.3.3.1 1H NMR OF URINARY SAMPLES OF LEC RATS BEFORE AND DURING

THE ONSET OF HEPATITIS 116

D.3.3.2 METABOLIC CHANGES IN LIVER OF LEC RATS AT DIFFEENT

HEPATITIS STAGES 119

D.3.3.2.1 Metabonomic analysis of lipid extracts of LEC rat liver samples 119

D.3.3.2.2 Metabonomic analysis of aqueous extracts of LEC rat liver samples

121

D.3.3.3 QUANTITATIVE RELATIONSHIP BETWEEN METABONOMIC AND

TRANSCRIPTOMIC ANALYSES 124

D.3.4 DISCUSSION 124

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E. GENERAL DISCUSSION AND PERSPECTIVES 131

F. REFERENCES 139 G. ANNEXES 151 H. PUBLICATIONS 173

19

PROLOGUE

This project was part of a collaboration between France and Mexico that has been

supported by the ECOS-ANUIES-CONACyT program (project: M02-S01 “Education

in DNA array technology and practical application in functional genomic in eukaryotic

cells”). The interest in studying the mechanism related to degenerative diseases is to

search novel approaches to early detection and treatment of those diseases.

The aim of this project was to define the gene expression and metabolic changes that

take place during the oxidative stress-related hepatitis and hepatocarcinogenesis. In

Toulouse (France) we found both the animal model and the technology to achieve this

objective.

The LEC rat model is considered a model of interest, because it is shown to generate

liver cancer related to inflammation and oxidative stress. LEC rats are bred in the

Institut National de la Recherche Agronomique (INRA), where the metabolism of lipid

peroxidation products (reflects of oxidative stress) and their biological importance are

studied. Using the “omics” technologies, it was possible to evaluate the global mRNA

expression and metabolic end products. These “omics” technologies, transcriptomics

and metabonomics, have been successfully developed in the institutions in where the

project took place: the microarray technology at the Institut National des Science

Appliquées (INSA), within the Toulouse Genopôle; and the metabonomics at INRA.

The knowledge and the experience of these groups in biochemical and functional

genomics areas, allowed me the use of these new approaches to formulate some new

hypothesis of oxidative stresss-induced hepatitis that could be further analyzed in the

effort to better understand pathologies as hepatitis and hepatocarcinogenesis.

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A. INTRODUCTION

23

A.1 THE CONCEPT OF OXIDATIVE STRESS The redox state of a cell is defined by the reduction potential (Eh) of all the redox

couples present in the cell, and because of its high abundance, glutathione (GSH)

represents one of the principal molecules that define the cellular redox potential

(Schafer et al., 2001). The redox environment of the cell can be estimated through the

redox state of the glutathione couple (GSSG/2GSH). Modifications in the reduction

potential of the GSSG/2GSH couple have been associated with the biological status of

the cell, for example, cell proliferation takes place at a Eh ≈ -240mV, differentiation at a

Eh ≈ -200mV; or apoptosis at a Eh ≈ -170mV (Schafer et al., 2001). These data point out

the importance of redox balance in biological systems, and is the basis of the oxidative

stress concept.

By definition oxidative stress is an increase in the reduction potential or a large decrease

in the reducing capacity of the cellular redox couples (Genestra, 2007). In other words,

oxidative stress is the result of an increased oxidized molecule production and a

diminution of their elimination. One group of these oxidized molecules responsible for

oxidative stress is the reactive oxygen species (ROS) family. As defined by their name,

ROS are species of oxygen which are in a more reactive state than molecular oxygen,

and by which therefore, oxygen is reduced to varying degrees. ROS compile a big

family of molecules, the most studied in biological systems are described in Table I.

ROS are produced by all aerobic cells. Under physiological conditions mitochondrial

production of ROS has been estimated for about ~2% of the total oxygen uptake by the

organisms (Inoue et al., 2003).

ROS can be produced by endogenous and exogenous sources (Figure 1). Endogenously,

ROS arise from mitochondria, cytochrome P450 metabolism, peroxisomes and

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OXIDANT REPRESENTATION CHARACTERISTICS

Superoxide anion O2-

One-electron reduction state of O2. Formed in many autoxidation reactions and by the electron transport chain. Rather unreactive but can release Fe2+ from iron-sulphur proteins and ferritin

Hydrogen peroxide H2O2

Two-electron reduction state, formed by dismutation of O2

- or by direct reduction of O2. Lipid soluble and thus able to diffuse across membranes. In aqueous solution can oxidize or reduce a variety of inorganic ions

Hydroxyl radical OH·

Three-electron reduction state, formed by Fenton reaction and decomposition of peroxynitrite. Has a very short in vivo half-life of approximately 10-9 s and a high reactivity, will attack most cellular components. It can damage virtually all types of macromolecules: carbohydrates, nucleic acids, lipids and amino acids

Organic hydroperoxide ROOH

Formed by radical reactions with cellular components such as lipids and nucleobases. Specific functional group or a molecule containin and oxygen-oxygen single bond (R-O-O-R)

Alkoxy, perory radicals RO·, ROO·

Oxygen centred organic radicals. Lipid forms participate in lipid peroxidation reactions. Produced in the presence of oxygen by radical addition to double bonds or hydrogen abstractions

Hypochlorite HOCL

Formed fron H2O2 by myeloperoxidase. Lipid soluble and highly reactive. Will readily oxidize protein constituents, including thiol groups, amino groups and methionine.

Peroxynitrite ONNO-

Is an oxidant and nitrating agent, formed in a rapid reaction betwen ·O2 and NO·. Lipid soluble and similar in reactivity to hypochlorite. Can damage a wide array of molecules in cells, including DNA and proteins: Protonation forms peroxynitrous acid, which can undergo homolytic cleavage to form hydroxyl radical and nitrogen dioxide

Table I. Principal ROS studied in biological systems. Adapted from Genestra (2007).

25

inflammatory cell activation (Conner et al., 1996; Valko et al., 2006). Xenobiotic

metabolism, ionizing radiation and cigarette smoking are examples of exogenous

sources for ROS production. The generation of some ROS is closely linked to cellular

redox-active metals, like iron and sometimes copper. Iron and copper content in cells is

a strictly controlled mechanism that ensures that there is no free intracellular iron or

copper ions. However under some stress conditions, release of copper or ion leads to an

uncontrolled ROS production (Leonard et al., 2004).

Figure 1. Sources of cell ROS production

26

Due to their high reactivity, ROS can react very quickly between each other and with

other molecules present in the cells. Interaction of ROS with cellular lipids, proteins and

nucleic acids are thought to be mechanisms by which oxidative stress modulates cellular

faith (Droge, 2002). Here I will describe some of the reactions of ROS with

biomolecules and the consequence of these interactions on cellular damage and

carcinogenesis. However, our current knowledge of ROS biochemistry show that they

have not only deleterious effects but they actually can act as second messengers in cell

signaling (Genestra, 2007).

A.1.1 Oxidative DNA damage

Among ROS, the hydroxyl radical (OH·) is one of the most reactive. OH· is known to

react with all components of the DNA molecule, including purine and pyrimidine bases

and the deoxyribose backbone (Dizdaroglu et al., 2002). Fixation of DNA oxidative

modifications may be involved in mutagenesis and hence in carcinogenesis. Different

types of tumor have been shown to present oxidative-related DNA damage. ROS-induce

DNA damage involves single- or double-stranded DNA breaks, purine and pyrimidine

oxidation and DNA cross-links. Other oxidative stress DNA-modifications include lipid

peroxidation-DNA adducts. Consequences of DNA modifications include transcription

modifications, induction of signal transduction pathways, replication errors and

genomic instability (Valko et al., 2006). Evidences of DNA-ROS interaction in tumors

have been demonstrated by measuring the levels of 8-hydroxyguanine (8-OHG) (Floyd,

1990). 8-OHG is actually considered a potential marker of carcinogenesis (Olinski et

al., 2003). Both mitochondrial and genomic DNA are susceptible of oxidative-related

DNA damage, and both are also related to tumor development (Klaunig et al., 2004).

27

Repair mechanisms of DNA modifications are well studied. The major pathway for the

repair of ROS-induced DNA modifications is considered to be the base excision repair

(BER) pathway (Cadet et al., 2000). Alterations in BER pathway caused by diet

modifications, inflammation or other factors are associated to human cancers, as seen by

a diminution in BER enzymes activities in lung and tobacco-associated cancer patients

(Tudek et al., 2006; Tudek, 2007), enforcing the importance of ROS-induced DNA

alterations in cancer development.

A.1.2 Lipid peroxidation

One of the well recognized targets of oxidative stress are cellular membranes.

Polyunsaturated fatty acid residues of phospholipids are extremely sensitive to

oxidation. Lipid peroxidation refers to the oxidative degradation of lipids. It is the

process by which lipids are attacked by reactive molecules (i.e. ROS) abstracting a

hydrogen atom from a methylene carbon in their side chain. The overall process of lipid

peroxidation consists of three stages: initiation, propagation and termination. Lipid

peroxidation products include alcanes like pentane and ethane (Valko et al., 2006),

aldehydes like 4-hydroxy-2-nonenal (HNE) and malondialdehyde (MDA) (Esterbauer et

al., 1991; Comporti, 1998), and isoprostanes like the 8-IsoPGF2α (8-IsoProstaglandin

F2α) (Pratico et al., 2004) (Figure 2).

Both MDA and HNE have the ability to interact with biomolecules as proteins and

nucleic acids. MDA is mutagenic in bacterial and mammalian cells, it reacts with DNA

to form the premutagenic pyrimido[1, 2-a]purin-10(3H)-one (M1G) adduct (Plastaras et

al., 2000). M1G adduct is mutagenic in E. coli, inducing transversions to T and

transitions to A (Fink et al., 1997). Similarly, HNE is genotoxic for hepatocytes and

cerebral endothelial cells. Treatment of hepatocytes with HNE leads to a spectrum of

28

Figure 2. Lipid peroxidation process. Figure adapted from Young and McEneny (2001) (Young et al., 2001).

29

DNA alterations from sister chromatide exchanges to microsomal aberrations (Eckl,

2003). HNE is considered as a second messenger of oxidative stress (Zarkovic et al.,

1999; Zarkovic, 2003). Cellular signaling leading to apoptosis, proliferation or

differentiation can be modulated by HNE depending on its intracellular concentration

(Yang et al., 2003). HNE has been associated to cancer initiation by direct DNA

adduction (Hu et al., 2002) and by inhibiting the enzymes related to BER (Feng et al.,

2004).

A.1.3 Protein modification by ROS

Protein oxidation results from the reaction of ROS with both amino acid side chains and

peptidic backbone. Oxidative damage to proteins can take place within almost all the

amino acids, being the most reactive with sulfur amino acid-containing proteins (Berlett

et al., 1997). Certain oxidations of proteins are reversible, mainly oxidative

modification of cysteine and methionine can be reversed by enzymatic systems like

glutaredoxin/glutathione/glutathione reductase systems or thiorredoxin/thioredoxin

reductase systems (Holmgren et al., 2005). This reversibility of protein oxidation is very

important in cell signaling pathways and it is considered one of the main mechanisms of

redox signaling (Droge, 2002). Changes in the local redox state of protein sulfhydryls

leads to conformational changes that, depending on the protein, can either diminish or

augment DNA binding activity, or promote protein complex formations which are

necessary for signal transduction or transcription to proceed (Allen et al., 2000).

Irreversible oxidized proteins are hydroxylated and carbonylated amino acid derivates.

These moieties are chemically stable; hence, protein carbonyl groups are used as

biomarkers of oxidative stress. Accumulation of protein carbonyls has been observed in

Alzheimer’s disease, diabetes, inflammatory bowel disease, among others (Dalle-Donne

30

et al., 2003). Oxidized proteins are generally less active, and are exposing hydrophobic

amino acids at their surface, leading to changes in protein conformation and activity

(Friguet, 2006).

Elimination of mild oxidized proteins is preferentially done by the 20S proteasome in a

ATP- and ubiquitin-independent manner (Grune et al., 1995; Grune et al., 2003b).

However, strong protein oxidation or/and under strong oxidative stress condition lead to

proteasome inhibition (Grune et al., 1995). Accumulation of damaged proteins due to

proteasome inhibition has been associated to metabolic perturbations, aging and

different pathologies including Parkinson and Alzheimer diseases (Hayashi et al., 1998;

Ciechanover, 2005; Reed et al., 2007), but little is known about proteasome inhibition in

carcinogenesis.

A.1.4 Cellular adaptive responses to oxidative stress

Living aerobic organisms have developed elaborate sequences of adaptative

mechanisms to regulate oxygen homeostasis. The “antioxidant network” as defined by

Chaudiere et al. includes non-enzymatic and enzymatic antioxidant defenses, cofactors

and metabolic pathways that act in an orchestrated manner in order to minimize the

damaging effects of ROS (Chaudiere et al., 1999).

Non-enzymatic antioxidants include small hydrophobic and hydrophilic molecules.

They act as chemical traps of oxidizing free radicals and ROS. Hydrophilic scavengers

are mainly represented by GSH and ascorbic acid, they are found in cytosolic,

mitochondrial and nuclear aqueous compartments. Hydrophobic antioxidants are found

in lipoproteins and cell membranes where they inhibit or interrupt chain reactions of

lipid peroxidation (Halliwell et al., 1993). They include vitamin E (α-tocopherol),

31

carotenoids and ubiquinol. Essentially, non-enzymatic antioxidants are a first defense

against free radical formation by quenching nonspecifically free radicals.

More sophisticate mechanisms for ROS control include enzymatic antioxidants.

Antioxidant enzymes include superoxide dismutases (SODs), which ensure the

enzymatic degradation of superoxide ion. Hydroperoxides are degraded by catalase

(CAT) and glutathione peroxidase (GPX) or ascorbate peroxidase. GPX and ascorbate

peroxidases are reductases whose reducing coenzymes are regenerated by NADPH

equivalents produced in metabolic pathways (Chaudiere et al., 1999). Thiol-containing

proteins such as thioredoxins, thioredoxin reductase, peroxyredoxins and glutaredoxins

are mainly implicated in reduction of oxidized proteins (Holmgren, 2000). Other

enzymatic systems are designed for ROS and ROS-oxidized molecules elimination.

Glutathione S-transferases (GSTs) inactivate endogenous lipid peroxidation products

(aldehydes such as HNE), quinines, epoxides and hydroperoxides formed as secondary

metabolites during oxidative stress (Hayes et al., 2005).

Cellular defense mechanisms of oxidative stress might be activated by cellular sensors

of oxidative damage. Activation of gene expression in response to oxidative stress is

related to transcription modulation by transcription factors like NFκB, Nrf2 and AP-1

(Allen et al., 2000; Scandalios, 2005). Direct signaling of H2O2 in the differential

regulation of antioxidant genes likely occurs via protein-DNA interactions in the Nrf2-

antioxidant responsive element (ARE) and via NFκB in the promoters of these genes.

For example, HNE induces thioredoxin system gene expression through Nrf2 signaling

in neural cells as an adaptive response to mild oxidative stress (Chen et al., 2005; Tanito

et al., 2007).

Cellular response face to oxidative stress is complex and depends on levels of ROS

production. While mild oxidative stress is related to physiological process such as

32

proliferation and differentiation, strong and uncontrolled oxidative stress leads to cell

death by apoptosis or necrosis (Droge, 2002). Whether ROS effect on cells is beneficial

or pathological depends on complex factors that are not fully understood. Nevertheless,

the role of free radicals, mainly ROS, in pathophysiology is evident. The aim of this

work is to focus on ROS-induced hepatitis as a model for ROS modulation of cell faith

in relation to hepatocarcinogenesis.

A.2. OXIDATIVE STRESS AND LIVER DISEASES

In humans, chronic liver diseases are generated by different causes. Chronic alcohol

consumption, viral infections, metabolic dysfunctions, xenobiotic exposition and metal

overload are factors associated with hepatitis and liver tumor development. Oxidative

stress seems to be a common mechanism involved in all these different hepatitis causes

(Vendemiale et al., 2001; Nagasaka et al., 2006; Seitz et al., 2006). Patients suffering

from hepatitis C manifest hepatic oxidative stress, condition that is exacerbated by

alcohol consumption (Wang et al., 2006). 8-Nitroguanine, a marker of DNA oxidation,

is highly formed in patients with hepatitis C. Furthermore, some hepatitis C virus

(HCV) carriers with normal alanine aminotransferase (ALT) levels in serum have

elevated levels of lipid peroxidation products and low levels of reduced glutathione

(GSH) in plasma. In these patients, a greater degree of oxidative stress markers

correlates with a more severe status of the disease (Vendemiale et al., 2001).

A.2.1 Wilson’s disease

Wilson’s disease is an autosomal recessive disorder of copper transport. Affected

individuals exhibit excessive copper accumulation in the liver and brain, deficient

holoceruloplasmin biosynthesis and a marked impairment in biliary copper excretion

33

(Linder et al., 1996). The gene defective in Wilson’s disease is the ATP7B gene which

encodes a P-type ATPase, the Wilson protein (WND) (Tanzi et al., 1993). WND is

responsible for copper transport from the cytoplasm to the trans-golgy network, where

copper is bounded to apo-ceruloplasmin to be excreted into plasma (Pfeil et al., 1999).

Mutations in WND results in misdistribution of the protein and in a defective

intracellular copper transport. As a consequence copper accumulates in cells, generating

ROS production and hence oxidative stress. Wilson’s disease patients suffer of recurrent

or fatal hepatitis and of neurological disorders. Wilson’s disease patients die either due

to progressive liver failure or neurological deterioration. Hepatocelullar carcinoma

(HCC) is a rare condition in Wilson’s disease, but some cases have been reported

(Iwadate et al., 2004). The low incidence of HCC in patients with Wilson’s disease may

be attributable to the significantly shortened life in untreated patients that does not allow

time for cancer to develop (Pfeil et al., 1999).

A.2.2 Hepatocarcinogenesis

Liver carcinogenesis is associated with chronic inflammation and cirrhosis. Persistent

infection with HCV is a major risk factor for development of HCC. Mechanisms

associated to HCV-related HCC include mitochondrial oxidative stress and metabolic

alterations in lipid and glucose metabolism together with continued cell death and

regeneration associated with inflammation (Koike, 2007). In alcohol-related

hepatocarcinogenesis, oxidative stress generated from alcohol metabolism,

inflammation, and increased iron storage are important mechanisms (Seitz et al., 2006).

Carcinogenesis is a complex multistage process in which both proliferation and cell

death are altered. According to observations made in models of chemical carcinogenesis

(mainly in liver) and in epidemiological findings, the process of neoplastic development

34

has been operationally divided into three defined stages: initiation, promotion and

progression (Farber et al., 1986; Trueba et al., 2004; Anisimov, 2007). Initiation follows

irreversible changes in the genotype of cells (i.e. mutations in critical genes such as

oncogenes and tumor suppressor genes). The promotion stage is characterized by the

clonal expansion of initiated cells by the induction of cell proliferation and/or inhibition

of cell death. This process results in the formation of identifiable focal lesions

(preneoplastic lesions). Progression involves cellular and molecular changes that occur

from the preoneoplastic to the neoplastic state. This stage is mainly characterized by

genetic instability and disruption of chromosome integrity (Klaunig et al., 2004).

During the first stages of carcinogenesis, cells acquire a resistant phenotype, by which

cells are resistant to physiological mechanism of cell growth regulation and also to

cytotoxic environments (Laconi et al., 2000). Hence, initiated cells might be more able

to proliferate and resist to apoptotic physiological stimulus. Such phenotype is seen in

cancer cells, and it is explained by overexpression of genes related to toxic metabolite

excretion like multi-drug resistant protein (MDR), GSTs and GSH-associated enzymes

(Tew, 1994). Liver carcinogenesis is a good example of the phenotypic resistance in the

pathogenesis of neoplasia hypothesis. During initiation, hepatocytes acquire the

expression of proteins related to GSH metabolism. Moreover two of these proteins, the

glutathione-s-transferase type pi (GSTP) and the γ-glutamyltraspeptidase GGT, are

actually considered as early tumor markers (Pitot et al., 1996).

Initiation stage of liver carcinogenesis is closely related to liver injury and consequent

cellular turnover caused by the different etiologies of hepatitis (McGlynn et al., 2005;

El-Serag et al., 2007). Mechanisms by which hepatocytes are transformed into

preneoplastic and neoplastic cells are not well understood, but the influence of ROS and

oxidative stress in this process becomes more evident. Because of the complexity of

35

carcinogenesis, it is difficult to explore the mechanisms of ROS-related

hepatocarcinogenesis in a gene by gene or isolated pathway matter, and a global

analysis of cellular and liver behavior under oxidative stress is necessary.

A.3 OMICS TECHNOLOGIES AND SYSTEMS BIOLOGY

A.3.1 Omics approaches, what do they mean?

From many years science used “reductionism” as tool to explain global phenomena.

Reductionism refers to the attempt of explaining complex phenomena by studying

functional proprieties of the individual components that compose a multicomponent

system (Strange, 2005). Even if these kind of studies remains and will remain essential

for biological understanding of specific situations in a phenomenon, they are not longer

effective in explaining the whole phenomena. Thanks to advances in biotechnology,

analytical chemistry and bioinformatics it is now possible to study, in a large scale, the

principal components of a biological system (Ivakhno, 2007). Genomics is defined as

the study of genes and their products in an organism as a whole. Functional genomics

goals include the integral study of the molecules that form part of the biological

systems, i.e. RNA, proteins and metabolites and which interactions and function lead

life to cells (Hocquette, 2005; Strange, 2005).

In order to understand functional genomics some definitions are necessary:

Genome: The complete collection of hereditary information of an individual organism.

This includes both genes and non-coding sequences of the DNA.

Transcriptome: Set of all messenger RNA (mRNA) molecules or transcripts produced

in one or a population of cells. It reflects the genes that are being actively expressed at

any given time.

36

Proteome: The entire complement of proteins expressed by a genome, cell, tissue or

organism. Specifically this term has been used to define the whole expressed proteins at

a given time point under defined conditions.

Metabolome: Complete set of small-molecule metabolites to be found within a

biological sample.

The global study of each of these fields has lead to new disciplines in biology:

Transcriptomics, proteomics and metabonomics for the study of the transcriptome, the

proteome and the metabolome, respectively (Hocquette, 2005). Together this new

approaches allow the description and comprehension of complex cellular networks in

which communication between genes, proteins and physiological adaptation through

metabolic profile changes are taken in account (Ivakhno et al., 2007). Omic approaches

are promising in understanding disease mechanisms and in the research of new early

markers for the detection of diseases.

A.3.2 Transcriptomics

The study of transcriptomics examines the expression level of mRNAs in a given cell

population, often using high-throughput techniques including differential display, serial

analysis of gene expression (SAGE) and cDNA or oligonucleotid arrays (Chittur, 2004).

The principle of arrays is simple: thousands of probes (DNA fragments corresponding

to different genes) are immobilized on a solid support (nylon membranes or glass slides)

and a specific labeled target made from RNA isolated from the sample of interest is

hybridized with the probes. The signal intensity of each individual probe should

correlate with the abundance of the mRNA complementary to that particular probe

(Brentani et al., 2005).

37

DNA microarrays have been used for the study of several pathologies. In the liver, the

principal objectives imply the research for markers that would allow early diagnosis of

diseases like cirrhosis or HCC to proceed (Chen et al., 2003; Nakatsura et al., 2003).

Using DNA microarrays coupled to unsupervised hierarchical clustering, Lee et al.

classified patients suffering of HCC, their results showed the power of transcriptomic

analyses in tumor classification and prognosis (Lee et al., 2004). Similarly, Perez-

Carreon et al. described a list of differential expressed genes implicated in experimental

chemical hepatocarcinogenesis (Perez-Carreon et al., 2006).

A.3.3 Proteomics

The main objective of proteomics is to quantify protein levels and their dynamic

changes. Proteins are mainly studied by physical separation using two-dimensional (2D)

electrophoresis. The resulting gel contains the proteome separated by the

physicochemical properties of proteins, molecular weight and isoelectric point. Most

current studies in proteomics are based on mass spectrometry to detect and identify

proteins (Spahn et al., 2004). Applications of proteomics for the discovery of

serological tumor markers for clinical use are promising (Poon et al., 2001). More

interestingly, using this kind of analyses, researches have been able to define set of

proteins sensitive to oxidative modifications caused by ischemia/reperfusion damage

(Cesaratto et al., 2005).

Methodological limitations such as difficulties with membrane-associated, very acidic

or very basic, very low or very high molecular weight and very low abundance proteins

are still causing difficulties in proteome study (Humphery-Smith, 2004); nevertheless,

technical improvements are promising (Bunai et al., 2005).

38

A.3.4 Metabonomics

The analysis of the final metabolic products is possible grace to high-output analytical

techniques like 1H nuclear magnetic resonance (1H NMR) and mass spectrometry (MS).

These technologies have lead to the characterization of metabolic modifications caused

by different stimulus such as drug treatment, diseases or xenobiotic exposition (Griffin,

2003). Metabonomics is defined as “the quantitative measurement of the dynamic

multiparametric metabolic response of living systems to pathophysiological stimuli or

genetic modification" (Nicholson et al., 2002). The objective of metabonomics is to

study all metabolites present in a biological system without any bias associated with the

choice of the metabolites to be studied. This helps the identification of metabolic

networks (Lindon et al., 2005; Teahan et al., 2006). One advantage of metabonomics is

the possibility of studying the effects on general metabolism by analyzing urine or

serum samples. This would make possible to follow the time-related metabolic

responses under specific conditions. This is a revolutionizing approach for clinical and

pharmacological applications (Fan et al., 2004).

Metabonomics has been used in the investigation of drug-induced hepatotoxicity, and it

is nowadays considered as a useful tool in pre-clinical and clinical studies in drug

development (Kleno et al., 2004; Mortishire-Smith et al., 2004). In an integrative

clinical approach, using transcriptomic and metabonomic analyses, factors involved in

bad prognosis of neuroendocrine cancers have been described (Fan et al., 2004; Ippolito

et al., 2005).

A.3.5 Systems biology: Integrating the puzzle

The function of living organisms cannot be addressed satisfactorily by looking at

molecules alone. Classical isolated molecule studies are very useful to explain how

39

molecules work, but are very poor in information of how molecules interact in complex,

multicomponent biological processes.

Systems biology is an emerging field of biological science. Its goal is to understand the

integrated function of the different parts of an organism in order to identify molecular

interaction networks on the basis of correlated molecular behavior observed in omic

studies (Strange, 2005). In order to achieve this ambitious aim, it is necessary to link

quantitative data from omic approaches with phenotypic observations. One of the

challenges of omic experiments is how to detect meaningful patterns in the massive

amounts of data generated and how to deduce experimentally testable functions from

those patterns.

Bioinformatics is a tool for biologists useful to aboard this problem; it merges biology

with dedicated statistics methods and databases to develop algorithms and statistical

methods necessary to access, manage and analyze large data sets and assess their

interrelationships (Werner, 2007). In order to better explore omic data, multivariate

statistical analyses have been successfully used (Perez-Enciso et al., 2003). It is out of

scope in this work to deep into the mathematical bases of multivariate statistical

analyses; a basic explanation of the methods used in this thesis is given in when used in

chapter D.2.

The integrative system approaches have great advantages. One is to bring the research

into a new vision of without an a priori formulation of hypothesis. In the past,

hypothesis-driven research was conducted with the aim of testing biological hypothesis

which emerged from previous studies or theoretical considerations. Nowadays, with

omic technologies, new hypothesis are generated with the scope of the key role of some

unknown molecules (Dmitrovsky, 2004). The idea is to search molecular signatures

40

(cluster of genes, of proteins, of metabolites, etc) which would characterize a biological

process or a specific phenotype.

A.4 THE LEC RAT MODEL

The general aim of this work was to define in an integrative approach the gene

expression and metabolic changes during the onset of oxidative stress-induced

hepatocarcinogenesis. In order to achieve this objective we chose Long Evans

Cinnamon-like color (LEC) rats as model.

LEC rats generate spontaneous acute hepatitis, fibrosis and liver tumors as a

consequence of an abnormal liver copper (Cu) accumulation and subsequent oxidative

stress (Mori et al., 1994). This rat strain shows chronic liver damage, hepatocyte death

and regeneration, at the promotion stage of carcinogenesis. Such a natural history of

HCC development in LEC rats is similar to that of human HCC.

A.4.1 Copper accumulation in LEC rats

LEC rat is an inbred strain of a mutant rat that was originally isolated from a closed

colony of Long Evans rats. LEC rats are characterized by excessive Cu accumulation in

the liver and impaired biliary Cu excretion (Li et al., 1991). Cu accumulation produces

great quantities of ROS, mainly the hydroxyl radical (OH·), which is believed to be the

origin of the acute hepatitis and the subsequent HCC that is observed in this rat strain

(Yamamoto et al., 2001).

LEC rats have a deletion in the gene homologous to the Wilson’s disease gene, the

ATP7B gene (Yamaguchi et al., 1994). Wu et al. identified the LEC Atp7b gene

mutation. The disease causing mutation is a deletion of 900 bp in the 3’ terminus which

removes the critical ATP binding domain of the wild type gene leading to a non

functional protein (Wu et al., 1994). Introduction of human ATP7B gene into LEC rats

41

restores biliary Cu excretion and prevents hepatic abnormalities, showing that Atp7b

gene mutation is solely responsible for LEC rat pathologies (Meng et al., 2004).

Hepatocyte Cu distribution in LEC rats progresses from being initially well distributed

in cytoplasm, bound to metallothionein (Cu-Mt complexes) to an accumulation of the

Cu-Mt complexes in the lysosome just before acute hepatitis. The acid conditions in the

lysosome result in the degradation of these complexes resulting in the formation of a

partially polymerized Cu-Mt containing reactive copper (Klein et al., 1998). This Cu

initiates lysosomal lipid peroxidation, leading to hepatocyte necrosis and fulminant

hepatitis.

A.4.2 Clinicopathological characteristics of LEC rats

LEC rats present elevated hepatic Cu levels, reduced biliary Cu excretion, haemolysis,

ceruloplasmin deficiency and increased hepatic iron levels. This mutant strain also

possesses reduced hepatic selenium that may induce a reduction in the antioxidant

capacity against copper induced free-radical damage (Downey et al., 1998).

Around 11-16 weeks old LEC rats suffer from an acute hepatitis period, with symptoms

of jaundice. Kasai et al. have described the clinicopathological characteristics of the

acute hepatitis in the LEC rat (Kasai et al., 1990). Around one week from the first

hepatitis sign, LEC rats suffer from a fulminant hepatitis period in which 40-50% of rats

die. The remaining animals can survive for more than 1 year with chronic hepatitis and

develop preneoplastic and neoplastic lesions of the liver such as hepatocellular

carcinoma (HCC) together with cholangiofibrosis (Figure 3). Most of the liver cancers

are histologically classified as well-differentiated HCC. Sex differences exist in

hepatitis index and liver tumor formation. Male LEC rats have more HCC formation

42

Figure 3. Clinico-pathological description of LEC rat spontaneous hepatitis and hepatocarcinogenesis. Photo: LEC rat (Cinnamon-like color rat) and Long Evans (LE) rat (Black and white color rat). Scheme adapted from Masuda, et. al. 1988 (Masuda et al., 1988).

43

frequency while females develop cholangiocarcinoma more frequently. Moreover,

hepatitis in female LEC rats takes place earlier than in males (Kasai et al., 1990).

The study of liver lesion incidence shows a sequential development of liver foci,

nodules and HCC similar to those seen in chemical hepatocarcinogenesis models.

Furthermore, the phenotype of preneoplastic and neoplastic lesions in LEC rat is the

same as in chemical hepatocarcinogenesis, with increased levels of the positive tumor

markers GSTP, GGT and α-fetoprotein (AFP) and decreased levels of the negative

markers glucose-6-phosphatase (G6Pase) and adenosine triphosphatase (ATPase)

(Sawaki et al., 1990).

A.4.3 Mechanism of hepatocarcinogenesis in LEC rats

Hepatocarcinogenesis in LEC rats is related to liver Cu and iron accumulation, since in

rats fed a low Cu and iron diet or treated with copper-chelating agents, preneoplastic

lesions almost disappear (Jong-Hon et al., 1993; Hayashi et al., 2004). Copper-

associated liver injury is regarded as resulting ultimately from oxidative stress. OH·

production in rats suffering from hepatitis is increased compared with Wistar rats and

LEC rats showing no signs of hepatitis (Yamaguchi et al., 1994). When LEC rats were

treated with the OH· scavenger D-mannitol, a decrease in ALT and bilirubin

concentrations together with a reduction in mitochondrial lipid peroxidation was

observed.

Lipid peroxidation products like HNE and MDA may be implicated in

hepatocarcinogenesis initiation in LEC rats. Increased mutagenic exocyclic DNA

adducts were observed in the liver of LEC rats. These adducts come from the addition

of lipid peroxidation products to DNA (Nair et al., 1996). Levels of etheno-DNA

adducts, 1,N6-ethenodeoxyadenosine and 3,N4-ethenodeoxycytidine, increase with age

44

reaching a peak at 8 and 12 weeks in nuclear and mithochondrial DNA, respectively

(Nair et al., 2005). Acute hepatitis in LEC rats impairs the repair of oxidative DNA base

damage by altering the expression of DNA glycosylases, endonuclease II and 8-

oxoguanine DNA-glycosylase which initiate the BER pathway (Choudhury et al.,

2003).

During hepatitis, p53 expression and hepatocyte apoptosis are higher in LEC rats than

in age matched control rats (Jia et al., 2002). Although p53 expression is increased, Ba

et al., using a yeast-based functional assay, demonstrated the presence of p53 mRNA

mutations in LEC rat liver. The authors suggest that during hepatitis, the cellular

damage degrades transcriptional fidelity and that mutations in p53 may have an effect in

p53 function and hence in cell cycle control (Ba et al., 2000). During chronic hepatitis,

there is a continuous cellular turnover. However the proliferation/apoptotic index ratio

indicates an imbalance in favor of cellular proliferation (Jia et al., 2002). The analysis of

G1 phase-related cell cycle cyclines suggest that while cycline D1 may be involved in

the regeneration of hepatocytes during chronic hepatitis, cyclin dependent kinase 4

(Cdk4) may play an important role in the development of HCC since it is significantly

increased in HCC compared to precancerous and chronic hepatitis LEC rat livers (Kita

et al., 2004). Together theses results indicate that during HCC development in LEC rats,

there is an increase in hepatocyte proliferation rather than a diminution in apoptosis, as

is the case in human HCC.

LEC rat is accepted as good model for Wilson’s disease. Several studies using different

compounds have been made in order to test their efficiency in diminishing hepatic

failure and HCC development. Cu-chelating agents like D-penicillamine, trientine

dihydrochloride and N-benzyl-D-glucamine dithiocarbamate show good results in

inhibiting hepatitis and HCC formation by preventing the Cu-dependent ROS

45

production in LEC rats (Jong-Hon et al., 1993; Hayashi et al., 2004; Shimada et al.,

2005). D-penicillamine is one of the treatments of choice for Wilson’s disease patients.

D-penicillamine not only prevents the development of hepatitis, but it can reverse the

hepatitis evolution by dissolving the polymerized Cu-Mt complexes and diminishing

ROS production (Klein et al., 2000). Antioxidant treatment has successfully diminished

the incidence of hepatic failure in LEC rats. N-acetylcysteine (Kitamura et al., 2005),

proline, ascorbic acid, thioredoxin in combined administration have significantly

delayed the appearance of jaundice and rat mortality (Hawkins et al., 1995).

47

B. AIMS OF THE STUDY

49

The interest of studying the hepatitis process using global approaches comes from the

idea to evaluate the changing phenotype that leads to hepatitis and cancer initiation

without any bias that could be produced by pre-formulated hypothesis. This would

allow us to search for new markers for early detection and treatment of hepatitis and to

increase the knowledge of oxidative stress-related mechanisms of disease and cell

regulation.

The general aim of this work was to define the oxidative stress-induced gene

expression and metabolic changes during the hepatitis development and the onset

of the hepatocarcinogenesis process.

In order to achieve this objective we have chosen the LEC rat model, in which ROS

induction is closely related to hepatitis and hepatocarcinogenesis development. As

hepatitis in this rat strain is spontaneous, its development is not synchronic. The first

goal of the project was then to define some biochemical parameters in order to get a

good classification of rats according to their hepatitis state and their degree of

oxidative stress.

Hepatocarcinogenesis in LEC rats has been well studied and it has been established as a

multistep process that follows an initiation-promotion-progression development of

preneoplastic and neoplastic lesions. Hepatocyte initiation is thought to take place

during hepatitis, but there are no clear results of it and some controversial data point out

hepatocyte initiation before hepatitis development as shown by increased levels of

tumor marker expression in hepatocytes in LEC rats without hepatitis. In order to define

the period of hepatocytes initiation we aimed to explore the tumor marker expression

in different LEC rat groups before and during hepatitis development.

The election of a suitable control group to compare both ROS-induced gene expression

and metabolic perturbations that lead to hepatitis and cancer initiation was a critical

50

stage in this work. We explored the use known preventive strategies to obtain a group of

rats that behave as normal rats without oxidative stress or hepatitis induction. To

validate D-penicillamine treated LEC rats as a suitable control group for

transcriptomic and metabonomic approaches, we evaluated the biochemical and

histological parameters of hepatitis development in these rats and we compared them

against non diseased LEC rats and Long Evans (LE) rats.

Transcriptomic changes in LEC rats towards hepatitis were defined using DNA

microarray approaches comparing those LEC rats at different hepatitis stages against the

control group of D-penicillamine-treated LEC rats. In order to explore the high amount

of data coming from transcriptomic data, multivariate statistical analyses were

performed. To define those changes in gene expression that might have an impact

in the development of hepatitis we used pattern recognition methods that allowed us to

discriminate between non diseased and diseased animals and to correlate the known

biochemical markers of hepatitis. Those selected genes would be useful for making

some new hypotheses concerning ROS-induced cell damage and cancer induction.

Changes in gene expression can lead to metabolic perturbations by changing enzyme

cell content. Moreover, ROS can directly modulate general metabolism by altering

enzyme and protein function. We were then interested in studying the metabolic end

product changes in a global context using metabonomic approaches. The evaluation

of small molecules in urine that might be related to hepatitis development may lead to

the identification of new markers for early detection that could be analyzed in a non

invasive way. On the other hand, the evaluation of changes in liver metabolome could

be useful for studying the mechanism of ROS-induced inflammation and cancer

initiation.

51

Systems biology refers to the integration of omic data in the objective of better

understanding the global function of cells and organisms. In an effort of better

understanding hepatitis and cancer processes, we aimed to integrate the data coming

from transcriptomic analyses to those coming from metabonomics. The correlated data

would be useful to define some of the cellular networks associated to ROS-induced liver

damage.

53

C. MATERIAL AND METHODS

55

C.1 ANIMALS AND TREATMENTS

Long Evans Cinnamon-like (LEC) rats were bred in our Institution animal facility from

rats kindly given by Dr. Matsumoto from Tokushima University (Japan). Male LEC rats

were maintained in metabolic cages from 6 weeks old until sacrifice at different

hepatitis stages. Male 5 weeks old Long Evans rats were purchased from Janvier (Le

Genest-Saint-Isle, France) and maintained for acclimatizing for 1 week before the

beginning of experiments at 6 weeks old. Untreated Long Evans rats were sacrified at

13 weeks old. For D-penicillamine-treated rats group, 6 LEC and 6 Long Evans rats

were administered with D-penicillamine in drinking water (100 mg/kg/day) for 6 weeks

until sacrifice at 13 weeks old. D-penicillamine (Sigma, Saint-Quentin Fallavier,

France) concentration was adjusted every three days according to water consumption.

C.2 URINE COLLECTION AND STORING

Urine samples were collected daily from rats housed in plastic metabolic cages. 0.5 ml

of a 360 mM butylhydroxytoluene (BHT) ethanolic solution was added to the urine

collection tubes that were placed in a unit maintained at 0 ºC during urine collection.

24-h collected urine samples were stored at -20 ºC until analysis.

C.3 TISSUE PREPARATION

Rats were sacrified by exsanguination under ether anesthesia. Blood was taken from the

aorta vein for plasma separation. Liver was excised, washed in physiological saline

solution. For RNA isolation, liver samples were immersed in RNAlater (Qiagen,

Courtaboeuf, France) following manufacturer’s instructions and stored at -80 ºC. For

histological examination, liver samples were fixed in 4% buffered formaldehyde,

embedded in paraffin. For GGT activity histochemistry, liver fractions were frozen by

56

immersion in liquid nitrogen-cooled 2-methylbutane and stored at -80 ºC. The rest of

liver samples were immediately frozen in liquid nitrogen and stored at -80 ºC.

C.4 HEPATITIS MARKERS IN PLASMA DETERMINATION

Aspartate aminotransferase (AST), alanine aminotransferase (ALT) activities and total

bilirubin (t-bil) levels in plasma were determined by the Toulouse Genopôle,

“Exploration physiopathologique”.

C.5 OXIDATIVE STRESS DETERMINATION

C.5.1 8-Isoprostaglandine F2α (8-IsoPGF2α) urine determination by Enzymatic

Immunoassay (EIA)

This assay is based on the competition between 8-IsoPGF2α and 8-IsoPGF2α-

acetylcholinesterase (AChE) conjugate (8-IsoPGF2α tracer) for a limited number of 8-

IsoPGF2α-specific rabbit antiserum binding sites (Pradelles et al., 1985; Wang et al.,

1995). Because the concentration of the 8-IsoPGF2α tracer is held constant while the

concentration of 8-IsoPGF2α varies, the amount of 8-IsoPGF2α tracer that is able to bind

to the rabbit antiserum will be inversely proportional to the concentration of 8-IsoPGF2α

in the sample. This rabbit antiserum-8-IsoPGF2α (either free or tracer) complex binds to

the anti-rabbit IgG mouse monoclonal antibody that is attached to the well. A

calibration curve from 7.2 pg/ml to 1 ng/ml is used. Samples were assayed in duplicates

with a 1:20 dilution. After an incubation period of 18 h with the specific antibody, tracer

is added for another incubation period during 4 h. Then, the plate is washed to remove

any unbound reagent and Ellman’s Reagent (which contains the AChE substrate) is

added to the well. The product of this enzymatic reaction is determined

spectrophotometrically at 414nm using a plate reader (Multiskan Ascent, Thermo

57

Labsystem, Cergy Pontoise, France). The color intensity is proportional to the amount

of 8-IsoPGF2α tracer bound to the well, which is inversely proportional to the amount of

free 8-IsoPGF2α present in the well during the incubation. All results were expressed on

the basis of 24 h excretion.

C.5.2 1,4-dihydroxynonane-mercapturic acid (DHN-MA) urine determination by

Enzymatic Immunoassay (EIA)

This competitive immunoenzymatic assay follows the same principle of that of the 8-

IsoPGF2α described above. EIA analyses were performed according to Gueraud et al

(Gueraud et al., 2006). Urine samples were assayed in duplicates with a 1:200 dilution.

A calibration curve was performed with a standard solution (32 pg/ml to 10 ng/ml of

DHN-MA). After an incubation period of 18 h at 4ºC with the specific antibody and

tracer, the plate was washed and the tracer fraction bound to the antibody was reacted

with Ellman’s reagent, which is the acetylcholinesterase enzyme substrate (tracer). The

resultant colored reaction was read at 414 nm using a plate reader (Multiskan Ascent),

color development being inversely proportional to the concentration of DHN-MA

measured. All results were expressed on the basis of 24 h excretion.

C.6 HISTOLOGY ANALYSES

C.6.1 Hematoxylin and eosin staining

5 µm liver sections were obtained from formalin-fixed paraffin samples with a

microtome, and sections were attached to silane-coated microscope slides. Slides were

deparaffinized and rehydrated as follows: two incubations of 5 minutes in xylene

followed by two incubations of 4 minutes in 100% ethanol, one incubation of 4 minutes

in 96% ethanol and one incubation of 4 minutes in 70% ethanol. Slides were then

58

washed in double distilled water. Sections were then stained with Mayer’s hematoxylin

for 6-10 seconds and immediately washed with tap water. Then slides were immersed in

acid ethanol (1% HCl and 70% Ethanol) for 2 minutes and then washed with tap water.

Subsequently, slides were stained with eosin for 1-3 minutes and then washed in

distilled water. After staining, slides are dehydrated and mounted with Canada balsam.

C.6.2 Immunohistochemistry of Glutathione S-transferase, placental form (GSTP)

Deparaffinized sections were blocked for 1 hour in 0.1% H2O2 in phosphate-buffered

saline (PBS), pH 7.4. Subsequently, they were incubated with commercial monoclonal

antibodies specific to glutathione S-transferase, placental form (GSTP,

DakoCytomation, Glostrup, Denmark) diluted 50 times in blocking buffer overnight.

After washing with PBS, the primary antibody was detected using an avidin-biotin

complex immunoperoxidase technique (Zymed Laboratories, Inc., Carlsbad, CA).

Slides were counterstained with hematoxyline and observed in light microscopy.

C.6.3 Immunohistochemistry of 4-hydroxynonenal (HNE)

In order to diminish the in situ formation of HNE, slides were incubated with H2O2 as

substrate for peroxidase only after antibodies attachment. Hence, deparaffinized

sections were incubated in methanol for two minutes then they were washed in PBS

three times of five minutes each. Subsequently, slides were incubated overnight with

monoclonal antibodies specific to HNE-histidine adducts diluted (1:5) in 1% bovine

serum albumin diluted in PBS. After washing with PBS, slides were incubated with 3%

H2O2 in PBS for 15 minutes in the dark. Then, slides were washed in TBS three times

for 5 minutes. Subsequently the primary antibody was detected using a Dako LSBA2

59

avidin-biotin complex immunoperoxidase system (DakoCytomation). Slides were

counterstained with hematoxyline and observed with a light microscopy.

C.6.4 Histological detection of gamma-glutamyl transpeptidase (GGT) positive

hepatocytes

GGT activity can be detected in frozen liver histological sections according to

Rutenburg et al. (Rutenburg et al., 1969). Representative 5 µm thick sections from 2-

methylbutane-protected frozen liver slices were obtained with a cryostat at -15ºC. Slides

were fixed in 96% ethanol for 5 minutes at -20ºC. Slides were then incubated for 20

minutes at room temperature in a reaction buffer containing 125 µg/ml gamma-

glutamyl-1-4-methoxy-2-nafthylamide (GMNA), 0.5 mg/ml glycil-glycine and 0.5

mg/ml fast-blue in 100 mM Tris pH 7.5. A red precipitate is then formed in GGT

positive cells. Slides were then rinsed with distilled water and incubated in 0.1M cupric

sulfate for 5 minutes at room temperature. Finally, slides were rinsed with distilled

water and dried. Slides were counterstained with hematoxyline and observed with a

light microscopy.

C.7 PROTEASOME PEPTIDASE ACTIVITIES

C.7.1 Cytosol extraction

10% (w/v) liver homogenates were prepared in 50 mM Tris-HCl pH 8.0 containing 0.1

mM EDTA and 1 mM β-mercaptoethanol. Homogenates were centrifuged at 100,000 g

for 1 h at 4 ºC; supernatants were stored at -80 ºC until use.

60

C.7.2 Proteasome activity assay

Chymotrypsin-like (ChT), trypsin-like (TL) and peptidylglutamyl peptide hydrolase

(PGPH) proteasome activities were determined with fluorogenic synthetic peptides N-

Succinyl-Leu-Leu-Val-Tyr-7-amido-4-methylcoumarin (LLVY-AMC), BOC-Leu-Ser-

Thr-Arg-7-amido-4-methylcoumarin (BOC-AMC) and Z-Leu-Leu-Glu-β-

naphthylamide (Z-LLG-NA), respectively. Assays were performed as described by

Fataccioli et al. (Fataccioli et al., 1999). Briefly, cytosolic fractions containing 150 µg

of protein were incubated with fluorogenic substrates (50 mM) and SDS 0.06 % in 150

mM Tris-HCl pH 8.0 for 30 minutes at 37 ºC. The reaction was stopped by adding 1 ml

SDS 1% and 2 ml 0.1 M sodium borate pH 9.1. The peptidase activity was determined

fluorometrically by measuring the release of 7-amino-4-methylcoumarin (λexc = 370 nm,

λem = 430 nm) and 2-naphthylamine (λexc = 323nm, λem = 400 nm) in a Jobin Yvon

spectrofluo JY3D fluorometer (Horiba Jobin Yvon, Longjumeau, France). A standard

curve of fluorescence for 7-amino-4-methylcoumarine and 2-naphthylamine was used to

calculate the concentration of liberated products in the assay.

C.8 SDS-PAGE AND IMMUNOBLOTTING

Cytosolic protein fractions were separated by SDS-PAGE using a mini-PROTEAN II

electrophoresis cell (Bio-Rad, Marnes la Coquette, France). Following transfer, the

nitrocellulose membranes were probed with antibodies to proteasome C2 subunit,

PROS-30 (Santa Cruz Biotechnology, Inc., Tebu-Bio, Le Perray en Yvelines, France),

or α-Tubulin (Clone B-5-12) (Sigma) diluted 1:300 and 1:1000 respectively. Antibodies

were visualized using the ECL chemiluminescent system from Amersham Biosciences

(GE Healthcare, Ramonville Saint-Agne, France).

61

C.9 RNA EXTRACTION

Total RNA extraction from RNAlater-stabilized liver samples was performed following

the instructions from Qiagen RNeasy mini kit using the On-Column DNase digestion

set (Qiagen, Courtaboeuf, France). Quality and quantity were determined by capillary

electrophoresis on the bioanalyzer from Agilent (RNA 6000 Nano Assay, Agilent,

Massy, France) and by spectrometric analysis on a NanoDrop ND-1000

spectrophotometer (Nyxor Biotech, Labtech, Palaiseau, France). Only samples with λ260

nm/λ280 nm ratio above 1.9 and rRNA28S/ rRNA18S ratio above 1.7 were taken for DNA

microarray and quantitative RT-PCR assays.

C.10 QUANTITATIVE RT-PCR (QRT-PCR)

2 µg of total RNA were reverse transcribed using SuperScript II-reverse transcriptase

system (Invitrogen, Cergy Pontoise, France) following manufacturer’s instructions.

Quantitative PCR assay for cDNA was carried out using TaqMan Universal PCR master

mix in a AB PRISM 7000 sequence detection system (Applied Biosystem, Courtaboeuf,

France). The fluorescent probe for rat glutathione S-transferase pi (GSTP) gene, probe

code: Rn00821792_g1, was used. Data were normalized against beta actin gene

expression, probe Rn00667869-m1 (Applied Biosystem). Signals were quantified using

a standard curve made by serial dilutions of a cDNA pool from all samples.

C.11 MICROARRAY ANALYSIS

C.11.1 DNA microarray slides

DNA microarray glass slides were made at Biochips Platform of Toulouse Genopôle

(Toulouse, France). Rat 70mer oligos from Operon_V3_rat oligo set (Operon

Biotechnologies, Cologne, Germany) were loaded at 300 pmol/well. Oligos were

62

resuspended in 20 µl 3× SSC buffer (final concentration 20 µM). Spotting was

performed as single spot per probe on Ultra GAPS glass slides (Corning, Fontainebleau,

France) using Genetix Qarray mini robot (TeleChem International Inc., Saint Marcel,

France). Atmosphere moisture was set at 50% and plates were cooled to 15 ºC to

prevent drying. Spotting design was as follows: 48 grids, each grid containing 24×25

spots. Each slide bears 28000 spots corresponding to 27004 transcripts and 22012 rat

genes.

C.11.2 Preparation of labeled cDNA

Total RNA (5 µg) was used for cDNA preparation. Reverse transcription was made

using ChipShotTM reverse transcriptase from Promega (Promega, Charbonnieres Les

Bains, France) using Cy3- and Cy5-dCTP from Amersham Bioscience. cDNA

purification was made by the Promega ChipShotTM labeling Clean-up system. Dye

incorporation was checked by measuring the absorbance at 260 and 550 nm for Cy3 and

260 and 650 nm for Cy5 in a NanoDrop spectrophotometer (Nyxor Biotech).

C.11.3 Array hybridization and scanning

Equal amounts of Cy3- and Cy5-labeled cDNA from control and tested groups were

hybridized for 8 hours at 42ºC in the automatic Hybridization Machine Discovery from

Ventana (Ventana Medical Systems, Illkirch, France). After hybridization, slides were

washed twice in 2× SCC (Ribowash buffer, Ventana Medical Systems) for 5 minutes

with agitation and then once with 0.1× SSC buffer. After washing, slides were dried by

centrifugation. Slides were scanned with Axon 4000A Microarray Scanner (Molecular

Devices, Saint-Grégoire, France).

63

C.11.4 Data analysis and public access

All data reported here are available to public, via NCBI-GEO platform, as both original

and normalized data. Image analysis was made by Genepix Pro V6 software. Each spot

was evaluated and selected to be analyzed by Bioplot software (http://biopuce.insa-

toulouse.fr). Spot analysis consisted in background subtraction with Lowess allowed

value 10, log transformation and Lowess normalization. The generated data set has been

submitted to the National Center for Biotechnology Information (NCBI) Gene

Expression Omnibus (GEO, http://www.ncbi.nlm.nih.gov/projects/geo/) database

(GSE7654).

Data analysis consisted of a variance analysis. The whole data set (27004 gene probes)

was subjected to an ANOVA using SPLUS 2000 software (MathSoft Inc., Seattle, WA).

Significance was set as P-value < 0.05. Partial Least Square (PLS)-2, PLS-Discriminant

Analysis (PLS-DA) and orthogonal signal correction (OSC) were made using SIMCA-P

8.0 software (Umetrics, Umea, Sweden). Oligos that contribute to axis building for both

PLS-2 and PLS-DA analyses were defined by the Variable Importance Projection (VIP)

criterion given by the SIMCA-P 8.0 software.

For biological organization of data, gene ontology (GO) classification was performed

by Gene Ontology Tree Machine from Vanderbilt University

(http://bioinfo.vanderbilt.edu/gotm) (Zhang et al., 2004).

C.12. METABONOMIC ANALYSES

C.12.1 Samples preparation for 1H NMR spectroscopy

Urine samples for NMR spectroscopy were prepared by mixing 500 µl of urine with

200 µl of phosphate buffer in D2O (pH 7.4) containing 10 mM deuterated

trimethylsilylpropionate as chemical shift reference (δ 0.0). The buffered urine samples

64

were then centrifuged at 10000 rpm for 10 min to remove any precipitates, and aliquots

of the resulting supernatant (600 µl) were placed in 5 mm NMR tubes.

Samples of liver tissue (~100 mg) were homogenized in 2 ml of 50/50 acetonitrile/H2O

containing 0.1% of BHT in an ice/water bath. The homogenates were centrifuged at

5000 g for 10 min at 4°C. The supernatants were removed and freeze dried before being

reconstituted in 1 ml D2O. 2 ml of 75/25 chloroform/methanol was added to the pellets

and extraction was followed by a subsequent centrifugation (5000 g for 15 min at 4°C).

The lipophilic supernatants were removed, dried under a stream of nitrogen and

reconstituted in 600 ml of CDCl3. The reconstitued solutions were transferred to 5 mm

(o.d.) NMR tubes.

C.12.2 NMR spectroscopic analysis

All 1H NMR spectra were obtained on a Bruker DRX-600 Avance NMR spectrometer

(Rheinstetten, Germany) operating at 600.13 MHz for 1H resonance frequency using an

inverse detection 5 mm 1H-13C-15N cryoprobe attached to a CryoPlatform (the

preamplifier cooling unit).

1H experiments of urine samples were recorded using the « Improved Watergate »

sequence for the suppression of the water resonance, accumulating 128 FIDs into 32 K

data points on a 12 ppm spectral width. All free induction decays were multiplied by an

exponential function equivalent to a 0.3 Hz line-broadening factor before Fourier

transformation.

The 1H NMR spectra of liver extracts were acquired at 300 K using a 1D single pulse,

accumulating 128 FIDs into 32 K data points on a 12 ppm spectral width. All free

induction decays were multiplied by an exponential function equivalent to a 0.3 Hz line-

broadening factor before Fourier transformation.

65

600 MHz 1H-1H correlation spectroscopy (COSY) NMR was performed on two aqueous

extracts samples to confirm the 1H NMR assignments. Transients were acquired into

1024 data points with 32 scans per increment, a spectral width of 7183 Hz, and 256

increments in the F1 axis, which was zero-filled to 512 prior to Fourier Transformation.

The relaxation delay between successive pulse cycles was 1.5 s. The data sets were

weighted using a sine-bell function in t1 and t2 prior to Fourier Transformation.

C.12.3 Spectra analysis

All NMR spectra were data-reduced using AMIX (version 3.1, Bruker Analytik,

Rheinstetten, Germany) to integrated regions 0.04 ppm wide corresponding to the

region δ 10.0 to 0.5. The regions δ 6.5-4.5 in the urine spectra and δ 5.0-4.7 in the

aqueous extracts were set to zero integral value to remove the variability of water

resonance suppression and cross-relaxation effects on the urea signal. For the lipophilic

extracts, the regions δ 7.5-6.8 (chloroform) and δ 3.6-3.3 (methanol) were removed

from all spectra.

Each integrated region (bucket) was considered as a variable for multivariate statistical

analyses.

C.13 STATISTICAL ANALYSES

Multivariate statistical analyses are described above. Univariate statistical analysis for

data from qRT-PCR, proteasome activities and immunoblotting are expressed as means

and standard deviations. Statistical differences were determined by ANOVA, followed

by Bonferroni’s post-hoc test.

67

D. RESULTS AND DISCUSION

69

D.1 CHAPTER 1:

CLINICAL AND BIOCHEMICAL CHARACTERISATION OF LEC RAT

MODEL

D.1.1 INTRODUCTION AND AIMS

D.1.2 EXPERIMENTAL PROTOCOL

D.1.3 RESULTS

D.1.3.1 HEPATITIS DEVELOPMENT IN LEC RATS

D.1.3.2 GGT AND GSTP EXPRESSION IN HEPATOCYTES OF LEC RATS

D.1.3.3 OXIDATIVE STRESS INDUCTION IN LEC RATS

D.1.3.4 EFFECT OF D-PENICILLAMINE ON HEPATITIS DEVELOPMENT

D.1.3.5 COMPARISON BETWEEN D-PENICILLAMINE-TREATED RATS

AND NORMAL RATS

D.1.4 DISCUSION

71


In order to achieve a better understanding of the role of oxidative stress in cancer,

suitable models in which direct association between both phenomena could be

demonstrated are necessary. In this context, LEC rat model for spontaneous hepatitis

and hepatocarcinogenesis is of interest (Mori et al., 1994).

This first step in our work aimed to define the biochemical and histological features in

LEC rat model in order to achieve a good classification of rats that allowed us to

describe the hepatitis process and at the same time to validate the group of D-

penicillamine treated rats as a control group for further studies in global approaches.

LEC rats are known to develop liver tumors. Carcinogenesis initiation stage in LEC rats

is thought to be at the onset of hepatitis, nevertheless, there is some controversial data

that suggest the acute hepatitis as a promoter stimulus for the cancer process. This

would mean that before the acute hepatitis, some already initiated cells might be present

and that during hepatitis those cells will be promoted with the hepatitis-regeneration

stimulus. In order to define the LEC rat cancer initation stage, we analyzed the tumor

marker expression in the liver of each LEC rat group.


D.1.2.1 Hepatitis determination:

Plasma ALT and AST activity determination by automated biochemistry

Liver structure analysis by H & E staining

D.1.2.2 Oxidative stress parameters

Determination of lipid peroxidation metabolites in urine

8-IsoPGF2α by EIA

DHN-MA by EIA

72

A. Hematoxylin and Eosin (H & E) staining of liver slides

B. Biochemical features and group classification

Figure 4. Histological and biochemical features of LEC rats at different hepatitis stages: Rats were classified according to their disease state, age and treatment. Values refer to means (SD= Standard Deviation). Means without a common letter differ as determined by ANOVA and the Bonferroni post-hoc test

Normal 6w Normal 9w Slight jaundice Jaundice F3,20 P

Age (weeks) 6 9 11-13 11-13 - -

ALT (U/l) 62.83(SD 13.7)a 66.7 (SD 7.7)a 584.8 (SD 120.4)b 1184 (SD 344.7)c 50.94 < 0.0001

AST (U/l) 123.8 (SD 15.3)a 95.5 (SD 7.4)a 479 (SD 144.7)b 725.7 (SD 208)b 33.94 < 0.0001

T-bilirubin (µmol/l)

2.1 (SD 1.8)a 1.9 (SD 0.8)a 107 (SD 64.7)a 369.7 (SD 228)b 12.87 < 0.0001

Liver histology Normal Normal architecture,

Slight hydropic change.

Abnormal. Conserved liver architecture; cholestasis;

presence of micronecrosis and apoptosis; inflammatory

infiltrates.

Abnormal. Necrosis, apoptosis, inflammation,

cholestasis, nuclear polymorphism, fat droplets.

- -

73

Determination of HNE-modified proteins by immunohistochemistry using the

monoclonal antibody anti HNE-His

D.1.2.3 Tumor marker expression

Determination of GGT activity in liver sections

Determination of GSTP protein by immunohistochemistry

D.1.3 RESULTS

D.1.3.1 Hepatitits development in LEC rats.

LEC rats were sacrified at different hepatitis stages, from normal to strong hepatitis

stage. This rat strain suffers of a period of acute hepatitis between 11-13 weeks with

symptoms of jaundice. Clinical symptoms of hepatitis included decrease in body

weight, yellowish skin on ears, tail and feet. During this period, activities of plasma

enzymes, alanine aminotransferase (ALT), aspartate aminotransferase (AST), as well as

total bilirubin levels, were significantly increased (Figure 4B). Contrary to “normal”

groups in which liver architecture was conserved (Figure 4), liver structure was strongly

altered during hepatitis periods with presence of cholestasis, apoptosis and

inflammation for the slight jaundice group and strong inflammation, hepatocytes

nuclear polymorphism and flat droplets (steatosis) in jaundice group.

74

Figure 5. Tumor marker expression in liver of LEC rats at different hepatitis stages: Liver sections were used to detect gamma glutamyltranspeptidase (GGT) activity by histochemistry (HC) or glutathione S-transferase, placental type (GSTP) by immunohistochemistry (IH) at different hepatitis stages.

75

D.1.3.2 GGT and GSTP expression in hepatocytes of LEC rats

Glutathione S-transferase (GSTP) and γ-glutamyltranspeptidase (GGT) are two well

known tumor markers in liver. In order to know in which moment cancer initiation stage

takes place in LEC rats, we analyzed those tumor marker expression in the liver of LEC

rats in each hepatitis period. Figure 5 shows histological determination of GGT activity

and GSTP protein presence in liver sections of LEC rats at different hepatitis stages.

GGT showed a time-dependent increase, which correlated to the state of disease. GGT

expression in Normal 6w and Normal 9w rat groups was confined to cells around bile

caniliculi. As hepatitis developed, GGT activity was detected in almost all hepatocytes

with no specific topographical distribution.

The evaluation of GSTP protein in hepatocytes by immunohistochemistry showed that

GSTP positive hepatocytes appear at the beginning of hepatitis process. Interestingly,

GSTP was present not only in the cytoplasm but also in the nuclei of hepatocytes in

Slight jaundice group. This GSTP presence in nucleus is transitory, since in Jaundice

group, GSTP is present only in the cytoplasm of hepatocytes groups presumed as

“initiated” cells. These results indicated that the cancer initiation stage takes place just

before the acute hepatitis.

D.1.3.3 Oxidative stress induction in LEC rats

Copper-associated liver injury is regarded as resulting ultimately from oxidative stress.

The majority of the OH· in vivo comes from the Fenton reaction Mn+ + H2O2 � M(n+1)+

+ OH· + OH-. The formation of OH· has been demonstrated to initiate the oxidative

degradation of lipids known as lipid peroxidation. 4-hydroxy-2-nonenal (HNE) is one

of the major products during lipid peroxidation. HNE is a reactive molecule that can

76

Figure 6. HNE-protein adduction in LEC rat liver at different hepatitis stages:

Immunohistochemistry for HNE-histidine adducts in liver sections of LEC rats at different hepatitis stages: A. Normal 6w, B. Normal 9w, C. Slight jaundice, D. Jaundice.

interact with biomolecules as DNA and proteins. HNE-protein adducts were visualized

by immunohistochemistry (Figure 6). HNE-positive hepatocytes were evaluated in each

group of rats, rats from the group Normal 6w presented HNE positivity in hepatocytes

around central vein or in the stroma. Just before the hepatitis in Normal 9w group, HNE

positivity was diffuse, not intensive but in a lot of hepatocytes. Positivity was both

cytoplasmic and nuclear or perinuclear, and in some cases nuclear or perinuclear

positivity was stronger than the cytoplasmic one. In diseased rat groups HNE-modified

proteins were also present. In Slight jaundice group, HNE positivity is mostly

cytoplasmic and sporadically positive picnotic nuclei were present. During the acute

phase of hepatitis, in Jaundice group, HNE positivity was diffuse in hepatocyte

cytoplasm without any topographic distribution, some apoptotic cells were also positive.

The main urinary metabolite of HNE in rat is the 1,4-dihydroxynone-mercapturic acid

(DHN-MA) (Alary et al., 1995; Alary et al., 1998). DHN-MA was assessed as a good

77

marker of HNE formation under oxidative stress conditions (Peiro et al., 2005). We

have measured DHN-MA in the urine of rat at the day of sacrifice (Figure 7-B). As

expected, DHN-MA urinary excretion increased 3.6 times in the Slight jaundice group

compared to the Normal 6w group of rats. However, levels of DHN-MA drastically

diminished in Jaundice group of rats, even when HNE was constantly produced in liver

as seen in the HNE-his adducts immunohistochemistry. DHN-MA is formed by the

metabolism of HNE originated from both cellular oxidative stress and from exogenous

sources like diet. During the acute phase of hepatitis, rat food consumption drops down

(Figure 8), so HNE coming from diet might also decrease and consequently levels of

DHN-MA. Accordingly to this hypothesis, a positive correlation between DHN-MA

levels and diet consumption was found (Figure 9). These results showed that DHN-MA

Figure 7. Lipid peroxidation products’ excretion in urine of LEC rats at different

hepatitis stages: Urine samples were collected from LEC rats. 8-IsoPGF2α and DHN-MA were measured by competitive enzymatic immunoassay (EIA). Values refer to means and standard deviations. Means without a common letter differ as determined by ANOVA and the Bonferroni post-hoc test

78

Figure 8. Diet consumption at different stages of hepatitis in LEC rats. 24 hours diet consumption was measured the day of rat sacrifice. Values refer to MEANS and Standard Deviation. Means without a common letter differ as determined by ANOVA and the Bonferroni post-hoc test.

Figure 9. Effect of diet consumption on lipid peroxidation products urinary

excretion: Correlation between diet consumption and 8-IsoPGF2α or DHN-MA urine excretion in LEC rats.

79

levels are very sensitive to exogenous HNE and then it was not a suitable marker for

endogenous oxidative stress when food consumption is not controlled.

Isoprostanes are other products of lipid peroxidation. Among them, 8-IsoPGF2α has

been described as a good marker of oxidative stress and inflammation. 8-IsoPGF2α

determination in urine showed an increase as hepatitis developed (Figure 7A). Levels of

8-IsoPGF2α were 6.5 times higher in Jaundice group as compared to Normal 6w group.

In Figure 9 we show the relation between 8-IsoPGF2α, DHN-MA and diet consumption.

When rats bear a normal food consumption, corresponding to non-acute disease, levels

of 8-IsoPGF2α were low, while as rats stopped eating, levels of 8-IsoPGF2α were high

because of hepatic inflammation.The contrary is seen with DHN- MA excretion because

of the decrease of food originating HNE consumption. These observations allowed us to

consider 8-IsoPGF2α determination as a better marker of lipid peroxidation in situations

of acute endogenous oxidative stress processes as in the case of LEC rats.

D.1.3.4 Effect of D-penicillamine on hepatitis development

Figure 10 shows the effect of D-penicillamine administration on hepatitis development

in LEC rats. As indicated by hepatitis markers on plasma, D-penicillamine stopped

hepatitis development in LEC rats (Figure 10B). D-penicillamine treatment lead to rats

with normal liver architecture, low levels of GGT activity and no presence of GSTP in

hepatocytes (Figure 10A). HNE-modified proteins were only seen around portal veins,

and urinary levels of 8-IsoPGF2α were diminished by 7.9 times in D-penicillamine

group of rats compared to non treated rats suffering from hepatitis.

80

A. Histological comparisons between un-treated jaundice LEC rats and D-penicillamine-treated rats

B. Effect of D-penicillamine on plasma hepatitis markers and urinary 8-IsoPGF2α

Group Jaundice D-penicillamine P Age (weeks) 11/13 13 - ALT (U/l) 1184 (SD 344.7) 45.7 (SD 2.7) 0.00047 AST (U/l) 725.7 (SD 208) 81.8 (SD 2.4) 0.00063

T-bilirrubin (µmol/l) 369.7 (SD 228) 2.3 (SD 0.6) 0.01092 Liver histology Abnormal. Necrosis, apoptosis,

inflammation, cholestasis, nuclear polymorphism, fat droplets

Normal. Slight hydropic change -

8-IsoPGF2α (ng/24h) 50.98 (SD 21.24) 6.43 (SD 1.84) 0.00050 Figure 10. Effect of D-penicillamine on hepatitis development, carcinogenesis initiation and oxidative stress in LEC rats: Values refer MEANS (SD = Standard Deviation), P = p value obtained by two-tailed student t test.

81

D.1.3.5 Comparison between D-penicillamine-treated rats and normal rats

In order to establish if D-penicillamine-treated LEC rats are a suitable control group for

further comparisons in global approaches (transcriptomics and metabonomics), we

compared these rats with the Normal 6w LEC group and a group of Long Evans rats

which are normal rats. Figure 11 shows the histological and biochemical comparison

between these groups. In all groups normal liver architecture, negative immunological

staining of GSTP and HNE-protein adducts were seen (Figure 11A). Plasma levels of

classical hepatitis markers were also low in all groups (Figure 11B). Together all these

results allowed us to choose D-penicillamine-treated rats as suitable control group in

further experiments since they behave as normal rats.

D.1.4 DISCUSSION

This first part of our study aimed to characterize hepatitis development in LEC rats on

the basis of observations of jaundice, biochemical parameters of hepatitis, and changes

in liver histology and to correlate the hepatitis process to oxidative stress and cancer

initiation processes. Since hepatitis was spontaneous, it differs from each rat and its

apparition was not synchronic, so rat classification was made according to different

hepatitis markers (hepatic enzymes and bilirubin in plasma and histological features),

rather than to a time concerning classification. We were able to classify LEC rats in 4

groups according to hepatitis stage: Normal 6w, Normal 9w, Slight jaundice and

Jaundice in which we observed significant differences between groups concerning

histological features, oxidative stress and tumor marker expression.

82

A. Histological comparisons between LEC rats 6w, D-penicillamine groups and Long

Evans

B. Plasma hepatitis markers

GROUP ALT (U/l) AST (U/L) T-BILIRUBIN (µMOL/L))

Normal 6w 62.83 (SD 13.7)a 123.8 (SD 15.3)a 2.1 (SD 1.8) LEC

D-penicillamine 45.7 (SD 2.7)b 84.8 (SD 2.4)b 2.3 (SD 0.6)

Long Evans 28.17 (SD 2.14)c 55.5 (SD 3.57)c 2.06 (SD 1.74) F2,15 22.74 56.28 0.2052 P < 0.0001 < 0.0001 0.8167

Figure 11. Histological and biochemical parameters comparison between LEC rats

groups (Normal 6w and D-penicillamine) and Long Evans rats. Values refer MEANS (SD = Standard Deviation). Means without a common letter differ as determined by ANOVA and the Bonferroni post-hoc test.

83

Kasai et al. have described the clinicopathological characteristics of the acute hepatitis

in LEC rats (Kasai et al., 1990). Similarly to their observations, we have showed that

the onset of the acute phase of hepatitis was characterized by an increase of apoptosis,

large nuclei and hepatocytes in mitosis, and that in the fulminant hepatitis period, liver

necrosis and inflammation were found together with an elevation of plasma enzyme

activities like ALT, AST and symptoms of jaundice.

LEC rats have been described to spontaneously develop liver tumors. Tumor formation

in this rat strain follows a natural history similar to those seen in chemical

carcinogenesis in which initiation, promotion and progression stages are followed by

the differential expression of proteins, known as tumor markers, in preneoplastic and

neoplastic lesions (Sawaki et al., 1990). Two of these known tumor markers are GGT

and GSTP. Our results showed that both proteins are expressed in hepatocytes at early

stages of hepatitis. GGT is an ectoenzyme that is expressed in fetal liver and in bile

canaliculi cells; in the adult rat liver, GGT is normally expressed only in the bile

canaliculi cells an in the canaliculi membrane face of hepatocytes. GGT activity was

detected in higher levels in Normal 6w rats than in Normal 9w group, even when

topographic expression of GGT was normal in both groups. This transitional increase of

GGT expression was first described by Jain et al. (Jain et al., 1992). They showed an

increase of GGT mRNA in 4 weeks aged LEC rats that was then diminished in 8 weeks

aged LEC rats. Moreover, GGT expression was higher in LEC rats than in Long Evans

Agouti (LEA) rats. The overexpression of GGT in hepatocytes has been associated with

stress conditions, such as oxidative stress (Yamada et al., 2006) and hepatitis (Paolicchi

et al., 2005), so the increase seen in the liver of Slight jaundice and Jaundice rat groups

might correspond to the liver disease state, and because of no specific topographical

84

distribution of GGT activity, it is not possible to associate GGT expression to the cancer

initiation stage.

GSTP is considered as one of the most reliable tumor markers in liver (Aliya et al.,

2003). GSTP is normally not expressed in adult rat hepatocytes and it is expressed in

almost 100% of HCC cells independently of the initiation-promotion stimulus. GSTP

expression in hepatocytes at early stages of hepatitis enforces the idea of initiation of

carcinogenesis at these stages. GSTs are mainly expressed in the cytoplasm, and their

involvement in xenobiotic biotransformation, drug metabolism and thiol homeostasis

has been well documented (Seidegard et al., 1997; Tew, 2007). GSTP is nowadays

considered as important in tumorigenesis and in cancer drug resistance (Tew, 2007).

GSTP has been shown to be induced as a cellular response to HNE and its expression

and activity is closely related to oxidative stress modulation (Fukuda et al., 1997). The

fact that we have found GSTP expression in nuclei of hepatocytes in early stages of

hepatitis (Slight jaundice group) remarks the importance of oxidative stress in cancer

initiation, since nuclear localization of GSTP has been associated with prevention of

apoptosis during oxidative stress conditions by conjugating lipid peroxidation products

as 4-oxo-2-nonenal with GSH and by consequence diminishing the formation of

exocyclic DNA products (Kamada et al., 2004). This suggests the induction of GSTP as

an adaptive response to oxidative stress. Further investigations may be done in order to

clarify the importance of this transitional expression of GSTP in hepatocyte nucleus and

its relation with liver cancer initiation.

Oxidative stress was clearly related to hepatitis development as demonstrated both by

increased levels of 8-IsoPG2α in urine and, directly in liver, by formation of HNE-

protein adducts. HNE is considered as the major lipid peroxidation product that has

been shown to have not only deleterious effects but also an important role in

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physiological processes as a second messenger of oxidative stress. Its presence is used

as a marker of oxidative stress in different tissues and cells during pathophysiological

processes (Zarkovic, 2003). HNE-modified proteins were visualized from young LEC

rats without signs of hepatitis (Normal 9w rat group). Modification of proteins by HNE

may be implicated in oxidative stress-mediated carcinogenesis, since it has been

demonstrated that HNE can inhibit some DNA damage repairing enzymes contributing

to DNA mutations and genome instability (Feng et al., 2004). HNE positivity was not

only in cytoplasm but also around the nuclei. This is the first report showing HNE-

modified proteins around hepatocyte nucleus. This interesting finding opens the door to

new insights of the HNE role in cellular homeostasis and carcinogenesis beyond to the

already known DNA-HNE adducts.

One of the aims of this first step of our work was to define a suitable control group of

rats that would be compared as normal rats in further experiments. LEA rat strain,

which is an Atp7b non mutant Long Evans deriving strain, is often used as control in

LEC rat studies, both rat strains deriving from a common ancestor (Long Evans rats).

However, LEA rats may have slightly evolved differently from LEC rats as indicated by

differences in epoxide hydrolase, GGT and UDP-glucoronyltransferase enzyme

activities and in cytochrome P-450 content between LE and LEA rats (Sugiyama et al.,

1988). Here we have used D-penicillamine in order to inhibit hepatitis development.

Histological and clinical features of these rats showed that they behave as normal rats,

according to the known effect of D-penicillamine to inhibit hepatitis and

hepatocarcinogenesis in LEC rats (Togashi et al., 1992; Klein et al., 2000).

Furthermore, we compared the histological features and the plasma levels of ALT, AST

and t-bilirrubin of D-penicillamine-treated LEC rats to those of untreated 6 weeks old

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LEC rats and to 13 weeks old Long Evans rats. D-penicillamine-treated LEC rats

behaved as normal rats with normal liver histology and negative staining for GSTP and

HNE-protein adducts, as well as low levels of hepatitis markers in plasma. Even when

we found significantly difference between LE and D-penicillamine-treated rats in ALT

and AST levels, D-penicillamine-treated LEC rats have the same genetic background as

untreated LEC rats, they were considered as a suitable control for studying the

mechanisms involved in hepatitis and cancer initiation in LEC rats by transcriptomics

and metabonomics analyses.

As a conclusion, in this first stage of our work we defined the rat groups that were then

used in transcriptomic and metabonomic analyses. Four groups of untreated LEC rats

were defined: Two non-diseased groups (Normal 6w and Normal 9w) and two groups of

diseased animals showing different hepatitis stages (Slight jaundice and Jaundice).

Together these groups of rats allow us to study the hepatitis development and the cancer

initiation stage in LEC rats, in comparison to a control group formed of D-

penicillamine-treated rats that were shown to behave as normal rats with no hepatitis

symptoms and no oxidative stress induction.

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D.2 CHAPTER 2:

TRANSCRIPTOMIC ANALYSIS OF LEC RAT HEPATITIS DEVELOPMENT



D.2.3 RESULTS

D.2.3.1 Microarray analysis and validation

D.2.3.2 Functional classification of VIP genes

D.2.3.3 Proteasome activity

D.2.3.4 Effect of D-penicillamine on gene expression

D.2.4 DISCUSSION

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Nowadays DNA-microarray technology is a powerful tool to determine the repertoire of

genes that are differentially expressed in cells or tissues exposed to different stimuli like

xenobiotic exposure or disease development. Microarray studies have produced detailed

pictures of gene expression in a sample, information that is not apparent in conventional

studies. The data sets are large and encompass new described genes. The problem is to

find relationships or discriminate which expression changes are important in such an

important mass of data. In these regard, multivariate statistical methods are of interest.

Biological information from transcriptomic data sets can be captured by correlating

them with other significant biological information using dedicated statistical methods.

In this part of our work, we have used Partial Least Squares (PLS) regression and PLS-

Discriminant Analysis (PLS-DA) to select genes that correlated to hepatitis symptoms

and that discriminated LEC rats at different hepatitis stages, respectively.


D.2.2.1 cDNA microarray analysis

D.2.2.1a RNA extraction

D.2.2.1b Preparation of labeled cDNA

D.2.2.1c Hybridization array: In Table I the hybridization design is shown. In

order to diminish the dye bias, caused by unequal amount of dye incorporation, the

array hybridization followed the dye swap design. Within the same group, three

independent samples were labeled with Cy3-dCTP and the other three were labeled with

Cy5-dCTP.

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Comparison

Group TEST CONTROL Image file name Chosen chanel

Ra0502 Ra0703 05-02Cy3-07-03Cy5 PMT550.grp green

Normal Ra0602 Ra0803 06-02Cy5-08-03Cy3 PMT550.grp red

6w Ra0702 Ra0903 07-02Cy3-09.03Cy3 PMT550.grp green

Ra2906 Ra1003 29-06Cy5-10-03Cy3 PMT600.grp red




Normal Ra0201 Ra1103 02-01Cy5-11-03Cy3 PMT520.grp red

9w Ra2406 Ra1003 24-06Cy3-10-03Cy5 PMT520.grp green




Ra0401 Ra0703 04Cy5 07Cy3 680b.grp red

Slight Ra0601 Ra0903 06Cy5 09Cy3 700b.grp red

Jaundice Ra1603 Ra0803 16-03Cy3-08-03Cy5 PMT500.grp green




Ra0501 Ra0803 08Cy5 05Cy3 650.grp red

Jaundice Ra1503 Ra1103 15Cy5 11Cy3 650.grp red

Ra1403 Ra1003 10Cy5 14Cy3 700.grp green

Ra1703 Ra1203 12Cy5 17Cy3 650.grp green




Long Evans Ra0205 Ra0803 02-05Cy5-08-03Cy3 PMT520.grp red






Long Evans Ra0805 Ra1103 08-05Cy5-11-03Cy3 PMT520.grp red

D- Ra0905 Ra1003 09-05Cy3-10-03Cy5 PMT520.grp green

penicillamine Ra1005 Ra0903 10-05Cy5-09-03Cy3 PMT520.grp red



Table II. Array hybridization design: Control group refers to D-penicillamine-treated LEC rats. Image file names are shown as they are designed in Bioplot software. The channel color was chosen according to test labeling. All samples were compared against the same control group.

D.2.2.2 Quantitative RT-PCR of GSTP gene

D.2.2.3 Proteasome peptidase activity determination in cytosol fractions

D.2.2.4 Proteasome protein determination by western blotting

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D.2.3 RESULTS

D.2.3.1 Microarray analysis and validation

The rat pangenomic microarray used in this study was made of 27004 oligo probes that

corresponded to 22012 rat genes. After scanning, images were analyzed by the Bioplot

software; Lowess-normalized data set with test/control (untreated/D-penicillamine

treated rats) ratios were obtained. In order to evaluate the difference between test groups

and the control group, means of ratio were calculated and after a student t-test, genes

were considered as differential genes when they showed a P < 0.05. In Figure 12 the

scatter plots of each comparison are shown. Each spot is represented as log10 of mean

ratio ± Standard Deviation as a function of the total intensity (Cy3 + Cy5). So the points

falling at the right corresponded to those genes with the higher expression in the

samples (high levels of mRNA) while those spots falling in the left were genes which

expression is more reduced. 1673 differential genes were found in Normal 6w/D-

penicillamine comparison, among them 880 genes were upregulated (log10 ratio > 0, red

spots) and 783 were downregulated (log10 ratio < 0, green spots) (Figure 12A). The

number of differential genes in the Normal 9w/D-penicillamine comparison was

diminished (777 differential genes), furthermore, the ratios of these differential genes

were small ratios, showing that the gene expression profiles in liver samples from

Normal 9w and D-penicillamine groups are quite similar (Figure 12B). As rats became

diseased, differences between test and control groups were stronger. 1702 differential

genes were founded in Slight jaundice/D-penicillamine comparison, among which 982

were upregulated and 720 downregulated. In the Jaundice/D-penicillamine comparison,

1817 differential genes were founded, 1035 upregulated and 782 downregulated (Figure

12C and D).

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Figure 12. Ratio/Intensity scatter plots: In black: non-differential genes (P > 0.05); in red: differential upregulated genes (P < 0.05 and log10 ratio > 0). In green differential downregulated genes (P < 0.05 and log10 ratio < 0). A. Normal 6w vs D-penicillamine comparison. B. Normal 9w vs D-penicillamine comparison. C. Slight jaundice vs D-penicillamine comparison. D. Jaundice vs D-penicillamine comparison. P values were calculated by student-t test.

In order to describe in a global context the gene expression changes during hepatitis

development, multivariate statistical analyses were performed. Normalized data sets

were filtered by ANOVA test when considering the group factor. From 27004 oligo

probes, 696 probes with a P value < 0.05 were considered as significant variables.

Probes were then subjected to an orthogonal signal correction (OSC) filtering in order to

remove strong structured variation in X matrix (transcriptomic data) that is not

correlated to Y matrix (class group) (Trygg, 2002). 89.88% of the information remained

after this correction. In order to discriminate between genes related to hepatitis

progression and those mainly explained by a age effect, a PLS-2 regression between the

696 statistically significant genes and plasma hepatitis markers ALT, AST and t-bil was

performed (Figure 13A). The goal of this method is to analyze or predict a set of

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dependent variables (matrix Y, i.e. plasma hepatitis markers) from a

Figure 13. Relation between hepatitis progression and gene expression. (A) 696 statistically significant variables from transcriptomic data (P < 0.05) were used for a PLS-2 regression against plasma hepatitis markers, ALT, AST and t-bil. (B) PLS-DA of transcriptomic data. Circles: rats within the same group. N6w: Normal 6w. N9w: Normal 9w. Sl-j: Slight jaundice. J: Jaundice.

set of independent variables (matrix X, i.e. microarray data) or predictors (Abdi, 2003).

PLS-2 regression showed a projection in which the Slight Jaundice and Jaundice groups

were well separated from the non diseased groups, Normal 6w and Normal 9w.

However, these two latter groups could not be separated between each other using this

analysis. The 696 genes were hence analyzed by a PLS-DA in order to refine the

separation between all groups of rats (Figure 13B). PLS-DA is a supervised

multidimensional statistical method that uses Principal Component Analysis (PCA)

94

principles, i.e. data projection into different dimensions, to find the components which

account for differences between groups. As shown in Figure 13B, the projection on the

first axis [t1] separated the non diseased rats (Normal 6w and Normal 9w groups) from

diseased rats, and even more, this mode of analysis was able to improve the separation

between groups of rats exhibiting a slight jaundice from those with a stronger disease.

The separation between groups was validated using another discriminant analysis, the

factorial discriminant analysis (FDA) (Figure 14), in which using only 10 variables

selected from the first 696 ones by a specific algorithm (Dumas et al., 2002), ∆2

Mahalanobis distances between all different groups were significantly different from

zero (P < 0.001) (Table III).

The oligo probes that contribute to axis building were defined using the Variable

Importance Projection (VIP) criterion given by the SIMCA-P 8.0 software. Statistical

VIP criterion is a measure which quantifies the influence on the response (on the latent

variable describing the projection of hepatitis development) of each variable summed

over all components and categorical responses relative to the total sum of squares of the

model (Perez-Enciso et al., 2003). It is a useful tool to select specific sets of genes that

distinguishes animals at different stages of hepatitis, making it an important parameter

to find possible pathways involved in disease development. From the 696 tested genes,

483 were considered as important for axis projection (VIP > 0.8) for the PLS-2 analysis

and 239 for de PLS-DA. 50% of the 239 genes found to be important for the PLS-DA

were also found to be important in the PLS-2 analysis, demonstrating that the separation

of groups in the PLS-DA is mainly due to hepatitis development. Table A

(supplementary data) provides the list of these 239 genes ordered by decreasing

importance of the VIP value.

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Figure 14. Factorial Discriminant Analysis (FDA) of transcriptomic data: 10 variables from 692 significative variables (P < 0.05) were selected as important for axes building. The first axis (LD1) explains the 66.9% of the variance while the second axis explains the 19.3% of the variance.

Table III. ∆

2 Mahalanobis distances between all different groups represented in FDA

test. The probability of these distances to be null is represented.

GSTP was found among the list of genes that significantly discriminated groups. We

confirmed the expression changes of this gene by the qRT-PCR method using β-actin as

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the reference gene (F3,23 = 54.82, P = 0.0104) (Figure 15). Therefore, this data together

with the fact that this gene is considered as a tumor marker known to be expressed in

LEC rats during hepatitis (Masuda et al., 1989) consolidated the validity of our

transcriptomic analysis of LEC rats during hepatitis development.

Figure 15. Confirmation of the differential expression of GSTP by quantitative RT-

PCR. Triangles: GSTP expression units from microarray data. Open circles: GSTP expression units from TaqMan QRTPCR assay. Data is expressed as ratio Test/Control ratio (means ± SD) where control is D-penicillamine-treated rat group. Means without a common letter differ as determined by a one-way ANOVA and the Bonferroni post-hoc test.

D.2.3.2 Functional classification of VIP genes

To gain more biological insights of our expression data, the list of 239 genes with a

significant VIP value (VIP > 0.8) were subjected to a functional classification based on

Gene Ontology annotation (GO). Only 40% of the total rat genome has a gene ontology

annotation (Lomax, 2005). This explained that only 59 genes had a GO annotation in

Biological Process ontology from the 239 selected genes (Figure 16). From these GO

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Figure 16. Gene ontology classification for 59 known genes. Classification by biological process was made (1-10). Chart data are read as: Category name (Accession): number of tested genes annotated in this category, percent of tested genes annotated in this category against total tested genes having a GO annotation.

98

Figure 17. Metabolism ontology sub-classification. Genes being part of metabolism classification were sub-classified according to their specific activity (1-25). Chart data are read as: Category name (Accession): number of tested genes annotated in this category, percent of tested genes annotated in this category against total tested genes having a GO annotation.

99

TABLE IV. Differential genes classified in GO: Protein Metabolism Description Symbol Oligo_id ENSEMBLE Gene_id Genbank Ratio p value

Normal 6w

Normal 9w

Slight jaundice

Jaundice

Cathepsin D precursor (EC 3.4.23.5)

Cts. Rn30018601 ENSRNOG00000020206 NM_134334 1.14a 1.09a 1.86a,b 2.44b 0.0207

Glycine N-methyltransferase (EC 2.1.1.20)

Gnmt Rn30014947 ENSRNOG00000016349 X06150 0.73a 0.85a,b 0.55b 0.41b 0.0050

C-reactive protein precursor Crp R002545_01 ENSRNOG00000000053 DW387982 0.68a 0.89a,b 0.45b 0.35b 0.0344 H-2 class II histocompatibility antigen, gamma chain

Cd74 Rn30017236 ENSRNOG00000018735 CB576583 1.06a 1.15a 1.73b 2.36c 0.0125

Meprin A alpha-subunit precursor (EC 3.4.24.18)

Mep1a R004430_01 ENSRNOG00000011022 BC081834 0.97a 0.95a 0.61b 0.92a 0.0351

proteasome 26S non-ATPase subunit 12

Psmd12 Rn30002776 ENSRNOG00000003117 BC083758 1.10a,b 1.01a 1.05a,b 1.48b 0.0160

Calpain small subunit 1 (CSS1) Capns1 Rn30001369 ENSRNOG00000001503 BC098068 0.94a,b 0.98a 1.11a,b 1.39b 0.0408 Serum amyloid P-component precursor (SAP)

Apcs R000224_01 ENSRNOG00000009086 X55761 1.01a 0.84a,b 0.41b 0.54a,b 0.0071

Complement C1q subcomponent subunit A precursor

C1qa Rn30011801 ENSRNOG00000012807 BC086605 1.00a 0.95a 1.67a,b 1.91b 0.0439

TGFB inducible early growth response 3 (predicted)

Gna14 Rn30013625 ENSRNOG00000014840 BC090316 0.90a 0.96a 1.24b 0.94a 0.0465

Kit ligand precursor (C-kit ligand) Kilt R003524_01 ENSRNOG00000005386 NM_021843 1.09a 0.96a 0.90a 1.46b 0.0406 Legumain precursor (EC 3.4.22.34) Lgmn Rn30006415 ENSRNOG00000007089 BC087708 1.05a,b 0.93a,b 1.36a 1.57b 0.0169 Ubiquitin-conjugating enzyme E2 B (EC 6.3.2.19)

UBC2_HUMAN

Rn30004545 ENSRNOG00000005064 AF144083 1.01a,b 0.99a,b 1.17a 1.52b 0.019

Proteasome subunit beta type 8 precursor (EC 3.4.25.1)

Psmb8 R004525_01 ENSRNOG00000000456 NM_080767

1.11a 1.07a 1.16a 1.27b 0.0317

Proteasome subunit alpha type 6 (EC 3.4.25.1)

Psma6 Rn30006440 ENSRNOG00000007114 NM_017283 1.10a 1.01a 1.16a,b 1.44b 0.0132

Proteasome subunit beta type 5 precursor (EC 3.4.25.1)

Psmb5 Rn30012312 ENSRNOG00000013386 XM_341314 0.93a 1.05a 1.15a,b 1.43b 0.0213

proteasome 26S, non-ATPase regulatory subunit 6

Psmd6 Rn30006048 ENSRNOG00000006751 BC059159 1.03a 1.03a 0.89a 1.60b 0.0306

Aminopeptidase B (EC 3.4.11.6) Rnpep R002043_01 ENSRNOG00000006720 AY724503 0.86a 0.95a 0.62b 1.11a 0.0465

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TABLE V. Differential genes classified in GO: Nucleobase, Nucleoside, Nucleotide and Nucleic acid Metabolism

Description Symbol Oligo_id ENSEMBLE Gene_id Genbank Ratio p value

Normal 6w

Normal 9w

Slight jaundice

Jaundice

Uricase (EC 1.7.3.3) Uox Rn30014939 ENSRNOG00000016339 M24396 0.69a 1.04a,b 0.57b 0.50b 0.0234 Growth arrest and DNA-damage-inducible protein GADD45 alpha

Gadd45a R001492_01 ENSRNOG00000005615 CO396252 1.00a 0.94a 1.06a,b 1.49b 0.0055

Signal transducer and activator of transcription 3

Sta3 Rn30018174 ENSRNOG00000019742 NM_012747 0.84a 0.86a 1.56b 1.72b 0.0498

Dynamin-2 (EC 3.6.5.5) Dnm2 Rn30006927 ENSRNOG00000007649 L25605 1.03a 1.06a 1.03a 1.58b 0.0151 Runt-related transcription factor 1, Acute myeloid leukemia 1 protein

Runx1 Rn30001533 ENSRNOG00000001704 L35271 0.93a 1.05a 1.27b 1.23b 0.0280

Cellular nucleic acid binding protein, Zinc finger protein 9

Cnbp1 R000749_01 ENSRNOG00000010239 CB743905 1.06a,b 1.09a 1.07a,b 1.45a,b 0.0298

DNA-repair protein XRCC1 Xrcc1 Rn30018333 ENSRNOG00000019915 AF290895 1.02a,b 0.98a 1.22a,b 1.54a,b 0.0338

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annotated genes, 38 fall into Metabolism, 19 in Organismal Physiological Process and

19 in Regulation of Physiological Process categories. A sub-classification in Cellular

Metabolism category revealed that genes implicated in Protein Metabolism, and in

Nucleobase, Nucleoside, Nucleotide and Nucleic acid Metabolism were overrepresented

(Figure 17). Values of expression changes of these genes and their associated function

are described in Tables IV and V.

Figure 18. Proteasome structure: 26S proteasome is composed of two subcompexes; a 20S core particule (CP) in which catalytic activity is present; and two 19S (PA700) particules in which recognition of ubiquitin and protein defolding take place. D.2.3.3 Proteasome activity

The proteasome (Figure 18) is a large, 26S, multicatalytic protease that degrades

polyubiquitinated proteins to small peptides. It is composed of two subcomplexes: a 20S

core particle that carries the catalytic activity, and the regulatory 19S particle

(Ciechanover, 2005). The main function of the proteasome is to degrade superfluous

and/or damaged proteins by proteolysis. Our DNA microarray data revealed that several

genes in the Protein Metabolism sub-category whose expression was increased in LEC

rats during hepatitis are implicated in protein degradation by the proteasome (Table IV).

Furthermore, a heat map of proteasome-related genes showed a tendency of

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upregulation of almost all genes that encode the 19S and 20S proteasome in Jaundice

group of rats (Figure 19). Among them, six genes were significantly increased. This

result suggested that the proteasome activity could be modified. To verify this

hypothesis, we determined the peptidase activities of 20S proteasome using specific

fluorogenic peptides as substrates n cytosolic fractions (Figure 20). Unexpectedly, there

was no significant modification of the chymotrypsin-like (ChT) proteasome activity

Figure 19. Heat map of proteasome-related genes in the liver of LEC rats at different

disease stages. Mean expression ratios of proteasome-related genes in each rat groups in respect to D-penicillamine-treated rats. Green and red represent downregulation and upregulation, respectively, as indicated in the “Ratio Scale” bar. (A) 20S proteasome-related genes. (B) 19S proteasome-related genes. * P < 0.05.

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(Figure 20A) even when the gene encoding the proteasome subunit responsible for this

activity, the Pmsb5 gene, was significantly up-regulated (Table IV). Moreover, we

found a significant decrease in trypsin-like (TL) proteasome activity in Slight jaundice

and Jaundice groups of rats (F4, 25 = 58.62, P < 0.0001) (Figure 20B). Likewise, the

peptidylglutamyl peptide hydrolase (PGPH) proteasome activity was diminished by

about 30% in the Slight jaundice group (Figure 20C), but restored in the Jaundice rat

group (F4, 25 = 13.01, P = 0.0075). Both genes responsible for TL (Psmb2 gene) and

PGPH (Psmb1 gene) had a tendency of being overexpressed during hepatitis (Figure

19A).

In order to evaluate whether changes in proteasome activities were due to variations in

proteasome protein levels, we evaluated the 20S proteasome levels in cytosol by

Western blotting. As shown in Figure 21, 20S proteasome levels were increased by 1.5

and 1.8 fold, respectively in Slight jaundice and Jaundice groups with respect to control

D-penicillamine group (F4, 25 = 10.86, P = 0.015). Thus, a decrease in TL and PGPH

proteasome activities does not correspond to a decrease in 20S proteasome levels but

rather to an inhibition of the intrinsic protein activity.

D.2.3.4 Effect of D-penicillamine on gene expression

In order to define if the treatment with D-penicillamine changes gene expression, Long

Evans (LE) rats were submitted to a treatment with D-penicillamine in the same way as

D-penicillamine-treated LEC rats. Both groups, non treated LE rats (LE) and D-

penicillamine-treated LE rats (LE-D-penicillamine) were compared against D-

penicillamine-treated LEC rats. From the LE/D-penicillamine comparison 3213 genes

were considered as differential genes (P < 0.05), 1799 were upregulated (log10 ratio > 0)

and 1414 were downregulated (log10 ratio < 0) (Figure 22A). When

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Figure 20. Changes in proteasome activities in the liver of LEC rats at different

disease stages. The proteasome activities in the liver of LEC rats in different groups were measured using the fluoropeptides, s-LLVY-AMC for the ChL activity (A), Boc-LSTR-AMC for the TL activity (B), and Z-LLE-βNA for the PGPH activity (C), as substrates. Each determination was performed at least in triplicate. Values refer to proteasome activity means ± SD. Means without a common letter differ as determined by a one-way ANOVA and the Bonferroni post-hoc test.

105

comparing LE rats treated with D-penicillamine against D-penicillamine LEC-rats only

413 differential genes were found, these genes might correspond to strain differences

(Figure 22B). Venn diagram allowed us to define those differential genes presumed to

be altered by D-penicillamine treatment. 3130 genes were modulated by D-

penicillamine (Figure 22C). Genes in VIP selection of the PLS-DA that might be altered

by D-penicillamine treatment are signaled in Table A (Annex 1).

Figure 21. Immunodetection of proteasome in liver of LEC rats at different disease stages and treatments. (A) Representative image of immunoblot analysis for 20S proteasome and alpha-tubulin proteins. (B) Densitometry analysis of immunoblot assay, values refer to proteasome/alpha-tubulin ratio means ± SD (n=6). Means without a common letter differ as determined by a one-way ANOVA and the Bonferroni post-hoc test.

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Figure 22. Effect of D-penicillamine on gene expression patterns. A. Scatter plot of Long Evans/LEC D-penicillamine comparison. B. Scatter plot of D-penicillamine-treated Long Evans/LEC D-penicillamine comparison. C. Venn diagram of both comparisons, 3130 genes were assumed to be deregulated due to D-penicillamine treatment in Long Evans rats.

D.2.4 DISCUSSION

A first evaluation of gene expression in LEC rats was made by Klein et al. in 2003

(Klein et al., 2003). From their transcriptomic analysis, authors have only retained

genes with the highest expression changes, and from these data, concluded on a major

role of oxidative stress, inflammation and DNA damage in hepatitis progression.

However, this mode of data analysis is too restrictive and there is no obvious

relationship between the levels of gene expression and the intensity of the disease since

alterations in a given cellular activity are usually not the result of a single but often

depends on the coordinated effects of multiple genes (Valafar, 2002).

107

To optimize the exploration of large data sets such as transcriptomic data in a given

biological context, multivariate statistical methods followed by Gene Ontology

classification represent powerful tools of analysis. We considered D-penicillamine-

treated LEC rats as a suitable control group for transcriptomic analyses. We checked in

LE rats the effect of D-penicillamine in gene expression. Even when there are some

modifications in gene expression patterns due to D-penicillamine treatment, D-

penicillamine had no effect in the genes here discussed.

PLS-2 analysis between microarray and plasma hepatitis markers showed a projection

of group of animals regarding hepatitis, which means a strong correlation between gene

expression changes and the development of hepatitis in LEC rats. This method was

effective to separate between non-diseased groups from jaundice groups, but it was not

pertinent enough to discriminate the two non-diseased groups (Normal 6w and Normal

9w), probably because the number of hepatitis markers is much reduced and their values

are very similar between these groups. PLS-DA takes the advantage of prior class

information to attempt to maximize the separation between groups of observations

(Perez-Enciso et al., 2003). All groups were significantly separated from each other by

PLS-DA. Furthermore, more than 50% of VIP-positive genes from PLS-DA were

common to those coming from PLS-2 regression demonstrating that PLS-DA group

separation is also well correlated to animal hepatitis development.

Statistical VIP criterion serves as a powerful tool to select the variables that have a

strong impact on the building of the axes in PLS methods. A high VIP value implies a

strong influence of the variable in the axes building (Perez-Enciso et al., 2003). GSTP

presented one of the highest VIP value (VIP value = 3.902), suggesting that the increase

in expression of this gene was very important for separation between groups of rats in

the PLS-DA. This finding agrees with the fact that we had found GSTP in liver of LEC

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rats during hepatitis. Furthermore, since GSTP is considered as one of the most reliable

tumor markers in liver (Sawaki et al., 1990), the idea of cancer initiation during

hepatitis in LEC rats is enforced.

During hepatitis progression in LEC rats, a strong oxidative stress takes place

(Yamamoto et al., 2001). Oxidation of biomolecules as DNA, lipids and proteins has

been extensively described (Yamamoto et al., 2001; Nair et al., 2005; Yasuda et al.,

2006). According to these observations, our analyses of microarray data show

enrichment of genes that belong to Protein Metabolism and Nucleobase, Nucleoside,

Nucleotide and Nucleic acid metabolism established by GO classification. Genes

involved in Nucleobase, Nucleoside, Nucleotide and Nucleic acid metabolism, and

genes involved in DNA reparation are up-regulated in Jaundice group. Growth Arrest

and DNA Damage 45-alpha (GADD45-alpha) is a nuclear protein involved in the

maintenance of genomic stability, DNA repair, and suppression of cell growth.

Overexpression of GADD45 gene has been reported in LEC rats at early stages of

hepatitis suggesting important DNA damage (Klein et al., 2003). The mechanisms by

which oxidized DNA bases are repaired include the base excision repair (BER)

pathway. M1G, etheno-DNA adducts, 8-hydroxyguanine and other oxidized base

lesions are removed by this pathway (Valko et al., 2006). Acute hepatitis in LEC rats

impairs the repair of oxidative DNA base damage by altering the activity of DNA

glycosylases, endonuclease II and 8-oxoguanine DNA-glycosylase, which initiate the

BER pathway (Choudhury et al., 2003). Interestingly, our results show overexpression

of DNA-repair protein XRCC1 gene. XRCC1 protein seems to play a central role in

coordinating the enzymes participating in BER pathway (Marsin et al., 2003).

Moreover, XRCC1 protein interacts with all enzymes of BER downstream of DNA-

glycosylase leading to the stabilization of the corresponding enzyme. Hence, up-

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regulation of XRCC1 in LEC diseased groups may contribute to BER pathway recovery

in LEC rats. Further investigations are needed to determine the role of XRCC1 in BER

pathway in LEC rats.

Protein Metabolism was the one category with the highest amount of associated genes.

The results may indicate an increase in protein degradation, since genes falling in this

category are genes related to ubiquitin-proteasome system and to other peptidases.

Increase in protein degradation could reflect protein damage as protein oxidation (Grune

et al., 1995). According to our results showing overexpression of genes involved in both

19S and 20S proteasome system, our initial hypothesis was that proteasome activity

should be augmented in Slight jaundice and Jaundice groups. However, TL and PGPH

20S proteasome peptidase activities were diminished in these groups. Protein levels of

proteasome were significantly increased in Slight jaundice and Jaundice groups

compared to D-penicillamine group, suggesting that the diminution of proteasome

activities may not be due to less proteasome proteins in cell, but rather because of a

direct inhibition of protein activity.

If oxidized proteins are not eliminated through proteasome pathway, they are able to

accumulate and to aggregate (Grune et al., 2003b). Oxidized protein aggregates are very

difficult to eliminate because they can not be unfolded, an important step in protein

degradation, and they are able to cross-link with proteasome subunits enhancing

proteasome inhibition. HNE, a major end product of lipid peroxidation, modifies

proteins leading to the formation of such protein aggregates (Grune et al., 2003a).

Furthermore, HNE can inhibit directly the proteasome activity by covalent binding to

proteasome subunits. Okada et al. (1999) have found similar results for the inhibition of

TL and PGPH peptidase activities under oxidative stress conditions in kidney. Authors

concluded that HNE-proteasome adduction was partially responsible for the proteasome

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inhibition under oxidative stress conditions (Okada et al., 1999). Although the detailed

mechanism of inactivation of proteasome with HNE is not fully understood, authors

suggest that the active site of the enzymes (TL and PGPH) may preferentially react with

HNE, leading inactivation. These findings lead us to propose, for the first time, the

inhibition of proteasome activities by oxidative stress during hepatitis progression in

LEC rats.

Inhibition of proteasome activities may induce the expression of proteasome genes in a

feedback mechanism that would compensate for the reduced protein degradation level.

Feedback regulation of proteasome genes have been described in Saccharomyces

cerevisiae. Proteasome gene expression was mediated by Rpn4 protein, which binds to

PACE (Proteasome associated control element) sequences that are found in the

promoters of proteasome subunits (London et al., 2004). Rpn4 protein is actually

degraded by the proteasome system, stabilization of Rpn4 by proteasome inhibition or

malfunction leads increased transcription of proteasome subunits by a negative feedback

circuit (Xie et al., 2001). In mammal cells, Meiners et al. (Meiners et al., 2003), have

studied the expression of proteasome genes after the inhibition of ChT proteasome

peptidase activity by specific proteasome inhibitors. They showed that there is a

concerted change in the expression of almost all the 20S proteasome subunits following

proteasome activity inhibition. Our results agreed with Meiners observations since even

when not all the genes were statistically significantly overexpressed, there was a clear

tendency for an upregulation of the majority of genes encoding for proteasome (Figure

5). The implication of a common transcription factor like Rpn4 in transcriptional

regulation of proteasome genes in mammal cells remains to be elucidated.

The proteasome is an essential organelle that participates in different cellular functions,

like apoptosis, survival and DNA reparation (Reed et al., 2007). Alterations in

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proteasome activity have been related to metabolic perturbations, aging and different

pathologies including Parkinson and Alzheimer diseases (Hayashi et al., 1998;

Ciechanover, 2005; Reed et al., 2007). Oxidized and damaged protein accumulation

may alter cellular functions, giving rise to deleterious effects on cellular homeostasis

that could be involved in initiation and progression of carcinogenesis (Friguet, 2006;

Cecarini et al., 2007). Interestingly, proteasome inhibition, either by specific inhibitor or

by iRNA, induces the expression of GSTP gene in epithelial liver cells. Mechanisms of

GSTP induction by proteasome inhibition seem to be different to Nrf2/Keap1 pathway

(Usami et al., 2005). Further studies are necessary to know if there is an association

between proteasome inhibition and GSTP upregulation in LEC rats, however the idea of

proteasome inhibition in cancer initiation is proposed.

In conclusion, using the LEC rat model, our work presents for the first time a genome-

scale analysis of hepatitis development and cellular responses due to copper-induced

oxidative stress. In this study, multivariate statistical analysis coupled to a biological

interpretation achieved by GO classification of the transcriptomic data allowed us to

focus on proteasome system and on the consequences of proteasome inhibition as a

main deregulated pathway that might be a determining factor during oxidative stress-

induced hepatitis and further cancer initiation. Our work shows an original and

functional way of analysis that illustrates the power of multivariate statistical tools to

explore microarray data to raise rational biological hypothesis.

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D.3 CHAPTER 3

METABONOMIC ANALYSIS AND ITS RELATION WITH

TRANSCRIPTOMIC DATA



D.3.3 RESULTS

D.3.3.1 1H NMR OF URINARY SAMPLES OF LEC RATS BEFORE AND

DURING THE ONSET OF HEPATITIS

D.3.3.2 METABOLIC CHANGES IN LIVER OF LEC RATS AT DIFFEENT

HEPATITIS STAGES

D.3.3.2.1 Metabonomic analysis of lipid extracts of LEC rat liver samples

D.3.3.2.2 Metabonomic analysis of aqueous extracts of LEC rat liver samples

D.3.3.3 RELATIONSHIP BETWEEN METABONOMIC AND

TRANSCRIPTOMIC ANALYSES

D.3.4 DISCUSSION

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Transcriptomic approaches are very useful to make a first exploration of understanding

gene function and pathology. To fully use the information generated by these analyses,

it is necessary to have a description of the changing phenotype so that gene expression

can be further understood in a biological context. In this regard, metabonomics

approaches provide a final link between gene expression and cellular phenotype

(Nicholson et al., 2002).

In LEC rats, metabolic perturbations that may predispose this rat strain to hepatitis and

hepatocarcinogenesis have been described. Depleted liver selenium and deficiency in

the activity of selenium dependent enzymes have been found (Downey et al., 1998).

Lipid metabolism is also deregulated in LEC rats, as an increased content of

triglycerides, free cholesterol and a reduction of bile acid export has been described in

12 weeks old LEC rats (Levy et al., 2007). Other perturbations have been explained as

consequences of oxidative environment in LEC rat liver. Diminution of protein-thiols

and of reduced glutathione, together with oxidative modification of glycoprotein’s

oligosaccharides during hepatitis are some examples (Samuele et al., 2005; Yasuda et

al., 2006).

Whether metabolic perturbations in LEC rats are associated with gene expression

changes during hepatitis and hepatocarcinogenesis development is difficult to explore

by analyzing case by case. Our aims in this part of the work were first, to determine

whether metabolic profiles obtained by 1H NMR could explain hepatitis development in

LEC rats; and second, to define canonical correlations between transcriptomic data and

final metabolic changes.

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D.3.2.1 Sample preparation for 1H NMR analysis: Urine and liver extracts were

prepared for 1H RMN analysis as described in the material and methods chapter.

D.3.2.2 Data analyses: 1H NMR spectra from 0.5 to 10 ppm were obtained and

cut in 0.04 ppm buckets. Quantitative variables were generated by integrating the area

under the curve of each bucket. From each 10 ppm spectra 220 variables were obtained.

Variables were then subjected to multivariate statistical methods. OSC was performed

to each group of data. Discriminant analyses (FDA and PLS-DA) were made in order to

evaluate the discrimination between metabolic profiles belonging to each LEC rat

group. PLS-2 regression between metabonomic data and hepatitis markers in plasma

was performed in order to associate metabolic changes to hepatitis development. Finally

a PLS-2 regression between liver metabolic and transcriptomic data was made with the

objective of explaining metabolic changes by changes in gene expression patterns in the

onset in LEC rats hepatitis.

D.3.3 RESULTS

D.3.3.1 1H NMR of urinary samples of LEC rats before and in the onset of hepatitis

Urine samples collected from non-treated and D-penicillamine-treated LEC rats were

analyzed by 1H NMR. Metabolic profiles were then analyzed and subjected to a

factorial discriminant analysis (FDA) (Figure 23). The first factorial axis given by this

analysis explained 41.9% of the variance and projection on this axis separates non-

treated from treated animals. The second axis explained 22.7% of the variance and

projection on this axis pointed out at the same time the development of hepatitis for the

non-treated animals and the metabolic changes due to the age of the animals.

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Figure 23. FDA of 1H NMR spectra of urine samples: Urine samples from rats housed in metabolic cages were analyzed from 6 weeks old until sacrifice. 50 discriminant variables were selected with a P < 0.05. Black arrow shows the trajectory from untreated animals towards hepatitis. Grey arrow shows the trajectory of D-penicillamine-treated rats.

The fact that we were able to discriminate rats that had received D-penicillamine from

those rats without treatment lead us to ask if these differences would be related to

difference in hepatitis development among the groups or to metabolic changes due to

the age of the animals. In order to resolve this question a PLS-2 regression between

urine 1H NMR metabolic profiles and plasma levels of hepatitis markers (ALT, AST

and t-bil) was made. In figure 24, the relation between changes in metabolic profiles and

plasma levels of hepatitis markers is shown. There was a good separation between rats

treated with D-penicillamine and non-treated rats. The first component explained the

45.1% of the variance from metabonomic data while the second component explained

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Figure 24. PLS-2 regression between 1H NMR metabolic profiles (t[1] axis) and

plasma levels of hepatitis markers (u[1]) axis

Table VI. Identified metabolites that were related to hepatitis development in LEC

rats: Variables were selected by a PLS-2 regression between metabonomics in urine and levels of hepatitis markers in plasma. cor: correlation coeficient

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85.4% of the variance from biochemical data. These results mean that the differences in

metabolic profiles in urine samples were related to disease development. 21 variables

with a correlation coefficient (cor) above 0.8 between metabonomic and biochemical

data were found. From these variables, only 4 were identified from 1H MNR spectra.

Taurine, acetamide, and isoleucine were positively correlated with biochemical

parameters, that means that when ALT, AST and t-bil plasma level augmented these

variables also augmented, while citrate was negatively correlated to hepatitis markers

(Table VI).

D.3.3.2 Metabolic changes in liver of LEC rats at different hepatitis stages

Since one of the main objectives of this work was to define a relation between

transcriptomic and metabonomic data, we analyzed the changes in metabolome directly

in liver samples of LEC rats at different hepatitis stages. Aqueous and lipophilic

extracts of liver were obtained and analyzed by 1H NMR.

In order to achieve a better correspondence between data from metabonomic and

transcriptomic analysis, metabonomic data were expressed as the test/control ratio

similarly to transcriptomic data. Test rats corresponded to non-treated LEC rats (classed

according to hepatitis stages) and control rats corresponded to D-penicillamine-treated

LEC rats. Ratios were obtained by comparing animals identically as done in microarray

slides (See Table II).

D.3.3.2.1 Metabonomic analysis of lipophilic extracts of LEC rat liver samples

Figure 25 shows the pattern recognition analyses of metabonomic data from lipophilic

(chloroform:methanol, 2/1,v/v) extracts of LEC rat liver samples at different hepatitis

stages. Data were subjected to an OSC after which 51.4% of the information remained.

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Figure 25. Pattern recognition analysis of

1H NMR spectra of lipophilic extracts from

liver samples of LEC rats at different hepatitis stages: A. PLS-DA. B. FDA of 10 discriminant variables selected by ANOVA with a P < 0.05. N 6w: Normal 6w; N 9w: Normal 9w; Sl j: Slight jaundice; J: Jaundice.

Table VII. Mahalanobis distances between groups from FDA analysis of

1H NMR

spectra of liver lipophilic extracts.

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Discriminant analysis of the data showed the trajectory of groups from non diseased

(Normal 6w and Normal 9w groups) to hepatitis (Slight jaundice and Jaundice groups).

However, discrimination of groups was not so evident in the PLS-DA (Figure 25A).

AFD with 10 discriminant variables was better to oppose non-diseased rats from

diseased rats in the first axis which explained the 79.9% of the variance between groups.

The probability of Mahalanobis distance to be null were very low (P < 0.001) (Table

VII).

D.3.3.2.2 Metabonomic analysis of aqueous extracts of LEC liver samples

Aqueous (acetonitrile:H2O, 50/50, v/v) liver extracts were subjected to high resolution

1H RMN and changes in metabolic profiles were then analyzed by pattern recognition

analyses (Figure 26). 60.1% of information remained after an OSC. Both discriminant

analyses (PLS-DA and FDA) showed a good separation of each group of LEC rats with

a trajectory that explained hepatitis development in the first axis. Furthermore, FDA

analysis using 10 discriminant variables explained the 81.7% of the variance between

groups in the first axis. The probability of Mahalanobis distance to be null were very

low (P < 0.001) (Table VIII). Variables that explained the axes building are shown in

Table IX.

Interestingly, the trajectory in which hepatitis development was explained by

metabonomic data of aqueous liver extracts was very similar of that seen by

transcriptomic data (Figure 14). So we focused on these data coming from aqueous

extracts to explore if there was any relation between metabolic profile changes and gene

expression changes due to hepatitis development.

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Figure 26. Pattern recognition analyses of

1H NMR of aqueous liver extracts of LEC

rats at different hepatitis stages: A. PLS-DA. B. FDA from 10 discriminant variables selected by ANOVA with a P < 0.05. N 6w: Normal 6w; N 9w: Normal 9w; Sl-j: Slight jaundice; J: Jaundice. Arrows indicate the trajectory on the axes from non-diseased to diseased rat groups.

Table VIII. Mahalanobis distances between groups from FDA analysis of

1H NMR

spectra of liver aqueous extracts

123

Table IX. Discriminant variables selected by ANOVA (P < 0.05) in a FDA: Metabolite associated gene ontology descriptions are shown.

124

D.3.3.3 Quantitative relationship between metabonomic and transcriptomic analyses

In order to define whether gene expression changes could explain the changes in

metabolic profiles seen in aqueous extracts of liver samples of LEC rats, a PLS-2

regression between transcriptomic data (as X matrix) and metabonomic data (as Y

matrix) was done (Figure 27A). The correlation between both data showed a linear

trajectory that explained hepatitis development. The first component for transcriptomic

data explained the 34.3% of the variance, while the first component for metabonomic

data explained the 30.6% of the variance between groups.

In order to know which variables were correlated between the two omics analyses, a

correlation matrix between both groups of data was done. From it, 39 variables with a

correlation coefficient above 0.85 were selected (Table B, annexes). The representation

of transcriptomic and metabonomic variables in a Cartesian plane showed positive and

negative correlation between variables (Figure 27B). This kind of analysis allowed us to

demonstrate that it was possible to associate changes in metabolic profiles (Y) due to

hepatitis development by changes in gene expression patterns (X). D.3.4

DISCUSSION

Metabolic perturbations related to copper accumulation and oxidative stress have been

extensively described. Copper induces ROS formation which in turns inhibit

mitochondrial enzymes like α-ketoglutarate dehydrogenase and pyruvate dehydrogenase

(Sheline et al., 2004). Deregulation in lipid metabolism was observed in a mouse model

of Wilson’s disease, in which the main modification was an inhibition of cholesterol

metabolism (Huster et al., 2007). Moreover, the production and metabolism of ROS is

highly regulated by metabolic pathways, the pentose phosphate cycle acting

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Figure 27 . PLS-2 regression between transcriptomic and metabonomic data. A. PLS-2, transcriptomic data (X matrix) was used to explain metabolic changes (Y matrix) in aqueous extracts of LEC rat livers at different hepatitis stages. B. Correlation between variables from transcriptomic data (green circles) and metabonomic data (orange boxes). Variables with a correlation coefficient > 0.85 are represented. N 6w: Normal 6w; N 9w: Normal 9w; Sl-j; Slight jaundice; J: Jaundice.

126

actually as the main metabolic response under oxidative stress with the production of

NADPH, a metabolite indispensable to maintain cellular reductive capacity (Tuttle et

al., 2007). Because alterations in physiological status by oxidative stress can break

homeostasis, resulting in ultimate changes in metabolites, metabonomic analyses are of

interest in studying and understanding the molecular mechanism of diseases.

Using 1H NMR coupled with pattern recognition techniques, we were able to

discriminate LEC rats undergoing hepatitis in both urine and liver samples.

Metabonomic analysis of urine samples showed that differences between untreated and

D-penicillamine treated LEC rats were related to hepatitis development as seen by its

good correlation with hepatitis markers in plasma. Furthermore, from the identified

urine metabolites (Table VI), taurine and acetamide groups were positively correlated

with plasmatic markers. Interestingly, taurine has been reported to be depleted in the

liver under oxidative stress due to chronic alcohol administration to rats (Kerai et al.,

2001), while hypertaurinuria has been observed when hepatotoxins were administered

to rats (Sanins et al., 1990). In our study on liver metabonomic data, taurine variable

was not selected. The acetamide group might be directly related to oxidative

degradation of bilirubin, as oxidative metabolites of bilirubin bearing an acetamide

function have been evidenced under oxidative stress conditions such as subarachnoid

hemorrhage (Kranc et al., 2000). The fact that pattern recognition techniques

discriminates diseased animals from non diseased in urine samples is promising while

new markers of hepatitis could be further identified and used as non-invasive tests.

Analysis of liver samples revealed that in both lipophilic and aqueous liver extracts,

metabolic changes are associated with hepatitis development as seen by a trajectory

from non diseased LEC rats (Normal 6w and Normal 9w groups) towards diseased LEC

rats (Slight jaundice and Jaundice groups). However, in this work, we focused on

127

aqueous extracts to understand metabolic perturbations regarding hepatitis and

transcriptomic data because of similar trajectories we obtained between metabonomic

and transcriptomic data (Figure 14 and 26). However further studies should be done in

order to identify and to explore changes in non polar metabolites.

Aqueous metabolites that were of importance for axis construction in FDA showed that

metabolic perturbations associated with hepatitis development included energetic

metabolism and amino acid metabolism (Table IX). Decreased levels of α-glucose lead

to the hypothesis of accelerated glycolysis. Perturbation of amino acid metabolism was

evident with increased levels of lysine and glutamate in Slight jaundice and Jaundice

groups. Enhanced glycolysis has been proposed as a protection mechanism face to

oxidative stress in cancer cells (Kondoh et al., 2007). In proliferating cells, transitional

augmentation of aerobic glycolysis and reduced ROS formation have been explained by

two cooperative effects: (a) generation of antioxidant molecules like pyruvate, citrate

and glutamate, and (b) prevention of excessive ROS production by keeping the rate of

mitochondrial glucose oxidation at basal levels (Brand, 1997; Wu et al., 2004). The

hypothesis of glucose enhanced metabolism as an adaptive response to oxidative stress

in LEC rat hepatitis is questioned and more studies should be made to test this idea.

Increase in lysine and glutamate could reflect the increase in protein catabolism

occurring during hepatitis.

Integrative biology refers to the integration of biological data to elucidate the cellular

network as a whole (Joyce et al., 2006). Integration of omics data is not easy to achieve

and requires the use of dedicated statistics and bioinformatics tools. Here we used PLS-

2 regression in order to correlate the metabolic changes in liver of rats suffering of

hepatitis by changes in gene expression patterns. Very interestingly, we found a linear

correlation between metabonomic and transcriptomic data associated with hepatitis

128

development and progression in LEC rats. This result might mean that both gene

expression changes and metabolic perturbations are closely related face to hepatitis

development. The correlation matrix formed by this analysis allowed us not only to

select new genes that were modulated by oxidative stress-induced hepatitis and that

were not evident in a single transcriptomic analysis, but also to formulate new

hypothesis about cellular adaptation to oxidative stress.

In a general context, the metabolites found to be correlated to gene expression changes

were mainly related to oxidative stress and to cellular adaptation to it. Increased levels

of oxidized glutathione, and amino acids related to glutathione biosynthesis (glutamate

and glutamine) reflect the oxidative conditions under hepatitis. While, as mentioned

above, diminution of glucose might reflect cellular protection to oxidative stress.

Genes related to these conditions might reflect the cellular responses or consequences

on general metabolic perturbations and not necessarily to a direct metabolite/gene

relation. Diminution of cytochrome P450 gene expression, as it is the case in our study

with the progression of hepatitis (Table B, annexes), has been described under oxidative

stress and inflammation. Since basal activity of cytochromes P450 leads to a continuous

ROS production, downregulation of cytochrome P450 genes has been considered as a

mechanisms to protect cells of deleterious effects of ROS (Morgan, 2001). Mig-6 gene,

which expression is also decreased with the progression of hepatitis (Table B, annexes),

is coding for an adapter protein that has been associated with proliferation and

differentiation control and is defined as an endogenous inhibitor of epidermal grow

factor receptor (EGFR) signaling (Xu et al., 2005). Down-regulation of this gene might

be associated with liver regeneration via hepatocyte proliferation as compensative

response to acute hepatitis. Alpha-methylacyl-CoA racemase gene, which is down

regulated with hepatitis in our study (Table B, annexes), is involved in the catabolism of

129

branched-chain fatty acids. Deficiency in this gene leads to decrease of bile acid

synthesis and consequently to choleostasis (Setchell et al., 2003).

The understanding of complex process like hepatitis and hepatocarcinogenesis is a big

deal in which several processes should be examined at the same time: apoptosis,

necrosis due to toxic effects of free radicals and in the other hand, inflammation and

hepatocyte proliferation as compensative mechanisms of liver damage. Metabolic

perturbations related to these events are not easy to elucidate, and the analysis of great

amounts of information is required. Thanks to 1H NMR metabonomic analyses we were

able to select, with a systemic approach, some metabolites that are related to oxidative

stress-induced hepatitis. However, low sensitivity and difficulties in peak interpretation

and molecule identification made the analysis restrictive to some identified metabolites

present in huge amounts. Nevertheless, we can not discard the importance of

metabonomic data, mainly as an integrated approach to transcriptomic data in

formulating new questions about mechanisms that lead to hepatitis development and the

association of hepatitis to cancer initiation.

131

E. GENERAL DISCUSSION AND PERSPECTIVES

133

The main objective of this work was to explore in a global scale the changes of gene

expression and their relation with perturbations in metabolic profiles in a model of

oxidative stress-induced hepatitis. Since acute hepatitis is closely related to liver cancer

initiation in LEC rats, perturbations in gene expression and in metabolic profiles could

also be analyzed within the scope of early modifications that can be associated with

carcinogenesis.

Transcriptomic approach led us to the construction of a database with the expression

levels of 27004 oligo probes which correspond to almost all the rat genome. This

database was used for the study of gene expression pattern and for the formulation of

new hypotheses. One of the problems that we were confronted to was the biological

interpretation of data, since only around 40% of genome has a defined function and

hence our analysis was restricted to this. However, using our transcriptomic database

and as advances in gene function definition arise, further analyses could be made in

order to find new insights about mechanisms of hepatitis and hepatocarcinogenesis.

Proteasome activity inhibition in LEC rats during hepatitis was a major finding; this is

the first report that implicates proteasome inhibition during hepatitis and its possible

consequence in hepatocarcinogenesis. We were able to achieve this finding thanks to

the original analysis of data that we performed. Two points should be underlined from

this finding: 1) Data analysis: data coming from transcriptomic analyses were

performed without any restriction of pre-made hypotheses or bias that could arise from

the exploration of a gene by gene analysis, where the observation of data is restricted to

modifications in “expected” or “known” genes. And 2) validation of hypothesis raised

from transcriptomic data is indispensable, not only by measuring the mRNA levels by

other techniques but by testing the activity of the proteins coded by the selected genes.

As it was the case for proteasome genes, where our initial hypothesis was an

134

upregulation in protein degradation because we found genes related to ubiquitin-

proteasome system upregulated, contradictory results could arise.

Protein degradation by proteasome is a key mechanism in protein turnover. Proteasome

activity deregulation may result in damaged proteins accumulation leading to toxic

insults to cells and to modifications in signal pathways. It could be therefore interesting

to evaluate the accumulation of oxidized proteins during the onset of hepatitis and to

find a correlation between this damaged protein accumulation and cell perturbations that

could results in cell cancer initiation. Oxidative stress-related modifications in

proteasome activity have been described in vivo in a model of kidney carcinogenesis.

Lipid peroxidation products such as HNE are directly implicated for proteasome activity

inhibition. As HNE has been described as a key molecule for oxidative stress-related

regulation of cell faith, it is also of interest to study the implication of HNE in ROS-

related inhibition of proteasome in liver. This would add more insights in the

mechanisms of ROS induced carcinogenesis.

In this work we were also interested in defining the changes in metabolic profiles that

lead to hepatitis. Our analysis of the metabolome based on NMR-metabonomics showed

a good correlation between changes in metabolic profiles and the levels of hepatitis

markers in plasma that lead us to conclude that there should be a metabolic response

face to oxidative stress and inflammation. However, the biological interpretation of

metabonomic analyses was limited because of the sensitivity of the analytical method

used to define metabolic profiles, the 1H NMR, and because of limitations in variable

identification. Even when pattern recognition analyses showed a very good

discrimination between groups with a trajectory that defined hepatitis development, the

variable identification led to the definition of only some metabolites. Moreover, 1H

NMR allows only the analysis of compounds that are present in huge amounts. We can

135

not discourage the use of 1H NMR for metabonomic analyses because it gives a

complete overview of well represented metabolites, but complementary analytical

procedures might be done, as mass spectrometry, in order to define some other

metabolites that are not concentrated enough to be seen by NMR.

Systems biology is a new discipline in biology that aims to integrate omic data between

each other and with other biological observations. Here, in an effort to better understand

the mechanism that lead to a hepatitis phenotype, we integrated the data coming from

transcriptomic techniques with those data coming from 1H NRM metabonomic analyses

in liver samples. PLS-2 regression between these two different data sets showed a linear

correlation between transcriptomic and metabonomic data in explaining the hepatitis

development. This result is very important since it would mean that both changes in

gene expression patterns and in metabolic profiles were closely correlated.

It was exiting to find correlation between transcriptomic and metabonomic data because

this may allow to new biological signatures implicated in ROS-induced liver diseases.

Furthermore, this is a significant advance in the discovering of new insights on

mechanism of hepatitis and in the discovering of possible early markers of

hepatocarcinogenesis. However, there was no evident biological relation between both

data (mRNA levels and metabolic profiles). It is worth to say that these kind of

canonical analyses allow us to explore the relationship between to set of variables, but it

does not mean a direct relation gene: metabolite. From our analysis, only one direct

relationship between gene and metabolite has been detected where aldehyde

dehydrogenase 18 is involved in glutamine degradation (Table B). However, some

hypotheses can be made and further investigated, for instance concerning the role of

cytochromes P450 in the development of hepatitis.

136

The expression levels of the genes that are directly related to the metabolites selected by

the PLS2 (genes which code for enzymes involved in their metabolic pathways) were

not significantly modified. Hence, there were not selected by the multivariate statistical

analyses. The fact that there were metabolic perturbations that explain the progression

of hepatitis, but that these perturbations could not be biologically explained by changes

in gene expression patterns lead us to suppose that there should be modification in the

enzyme levels or activity. Therefore it should be important to analyze the changes in

protein profiles that would be related to hepatitis development. Proteomics could be

used then as a complementary tool in order to gain complete information of cellular

response face to oxidative stress and inflammation. Using the whole data coming from

these three principal omic approaches (transcriptomics, proteomics and metabonomics),

we would be able to integrate the puzzle of the relationship gene: protein � protein:

metabolite that may explain complex processes like hepatitis and cancer initiation.

Furthermore, using proteomic analyses it would be not only possible to evaluate directly

the accumulation of oxidized proteins caused by perturbations in proteasome activity

but also to identify possible targets of ROS-induced protein modifications implicated in

hepatitis and cancer initiation processes.

Finally and as a general comment of this work I want to say that omic analyses are

nowadays very popular in biological and pharmaceutical research, their applications in

clinical and pharmacological investigations are promising, since we will be able to

study in a global scale the physiological or pathological cellular responses face to

diseases or to drug exposition, new insights in mechanism of diseases would be

elucidated, new biological markers of disease or toxicological effect would be found.

This new vision in biology implies the management and the interpretation of high

amount of data, and the integration of disciplines of different fields such as chemistry,

137

biology, physiology, informatics, mathematics is indispensable in the objective of

diminishing the reductionism vision of individual component study for explain complex

phenomena or to avoid the bias of “what we want to find” that could arise from pre-

made hypothesis.

139

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