predicting treatment response and the role of the isg15 ... · pathway to be involved in ifn...

Predicting Treatment Response and the Role of the

ISG15/USP18 Ubiquitin-like Signaling Pathway in Hepatitis

C Viral Infection

by

Limin Chen

A thesis submitted in conformity with the requirements for the degree of Doctor of Philosophy

Department of Molecular Genetics University of Toronto

© Copyright by Limin Chen 2010

ii

Predicting Treatment Response and the Role of the

ISG15/USP18 Ubiquitin-like Signaling Pathway in Hepatitis C

Viral Infection

Limin Chen

Doctor of Philosophy

Department of Molecular Genetics

University of Toronto

2010

Abstract

Hepatitis C Virus (HCV) infects 170 million people worldwide. The current treatment regimen,

which is combination therapy with pegylated interferon (PegIFN) and Ribavirin (Rib), cures only

50% of the patients infected with the most prevalent HCV genotype. Therefore, there is a

pressing need to understand the molecular mechanism of interferon resistance and to develop a

prognostic tool to predict who will respond to treatment before initiation of therapy. It has been

firmly established that the virus-host interaction plays an important role in determining treatment

outcomes. My thesis investigated the host factors that are involved in interferon resistance with

an aim to provide insights into the molecular mechanism of IFN resistance.

cDNA microarray analysis identified 18 differentially expressed hepatic genes from pretreatment

liver tissues of responders (Rs) and non-responders (NRs). Based on the differential expression

levels of these 18 genes, a prognostic tool was developed to predict who will respond to therapy,

with a positive predicting value (PPV) of 96%. Most of these 18 genes are interferon stimulated

genes (ISGs) and they are more highly expressed in NR livers, indicating that preactivation of

interferon signaling in the pre-treatment liver tissues contributes to NR. 3 out of the 18 genes are

iii

involved in an ubiquitin-like ISG15/USP18 signaling pathway that plays an important role in

interferon response. Over-expression of USP18 and ISG15 in the pretreatment liver tissues of

NR promotes HCV production and blunts interferon anti-HCV activity. There exists a distinct

cell-type specific ISG activation in the pretreatment liver tissues of Rs and NRs. Up-regulation

of the two ISGs that I tested (ISG15 and MxA) was found mainly in hepatocytes in NRs while

ISG activation was preferentially observed in macrophages in Rs.

Taking all these data together, pre-activation of interferon signaling and cell-type specific gene

activation in the pretreatment liver tissues of patients infected with HCV are associated with

treatment non-response. HCV exploits the host interferon system to favour its persistence by

enhanced replication /secretion stimulated by a few ISGs (ISG15, USP18) in response to IFN.

The developed prognostic tool can be used to stratify patients for treatment and the novel insights

of the molecular mechanism of IFN resistance in HCV patients offer potential drug targets for

future development.

iv

Acknowledgments

I wish to sincerely thank my supervisor Dr. Aled Edwards for allowing me to stay in his lab to

finish this interesting project. His support, guidance, encouragement, motivation, and supervision

are greatly appreciated. While providing me with freedom to work independently, Dr. Edwards

inspired me to achieve every step of my scientific goals and become a more productive scientist.

I would also like to wholeheartedly thank my research supervisor Dr. Ian McGilvray for assisting

me to overcome many obstacles that I have encountered during my PhD research. His input,

tutoring, encouragement and support, both emotionally and financially, will never be forgotten.

My supervisory committee members Dr. Jack Greenblatt, Dr. Rob Rottapel, and Dr. Daniela

Rotin have also been extremely helpful during this process due to their critical insights and many

valuable contributions for my thesis work. In addition, I would like to thank my collaborators,

Dr. Jenny Heathcote, Dr. Charles Rice and Dr. Glenn Randall for providing me with all the

necessary materials and reagents, including the most precious HCV liver biopsy samples and

HCV in vitro culture systems. Without their support this project could not have been completed.

I would also like to thank all the past and present members of Edwards/McGilvray laboratory

and management that assisted me in one way or another, with special thanks to Dr. Ivan Borozan,

Jing Sun, Larry Meng, Qiong Lin, Yan Chen and Max Ruzanov.

I wish to acknowledge the National CIHR Research Training Program in Hepatitis C (NCRTP-

HepC), the Canadian Graduate Scholarship (CGS), and the Canadian Institute for Health

Research (CIHR) for their financial support.

To my wife, Jing Sun, and my daughter and my parents for their love, support, understanding

and encouragement in making this long, but rewarding journey possible.

v

Table of Contents

Abstract ii

Acknowledgements iv

Table of Contents v

List of Tables ix

List of Figures x

List of Abbreviation xii

List of Publications xiv

Chapter 1: Introduction 1

1.1 HCV : virology, epidemiology and treatment 1

1.2 HCV and innate immune response: induction and evasion mechanisms 4

1.2.1 Innate immune response to HCV infection: Type 1 IFN production and

the Jak/STAT pathway 4

1.2.2 Adaptive immune response to HCV infection: humoral and cell-mediated

immune responses 6

1.2.3 HCV evasion from innate immune response: counteracting IFN pathways

and inhibiting NK cell activity 7

1.2.4 HCV evasion from adaptive immune response: lack of priming or

exhaustion of T cell response 8

1.3 Predicting treatment response: current approaches and limitations 9

1.4 Pre-treatment predictors of response in HCV: genomics-based approach 10

1.4.1 Hepatic gene expression 10

vi

1.4.2 Gene expression in blood 13

1.4.3 Host genetic polymorphisms 15

1.5 ISG15 and USP18 in viral disease 18

1.5.1 ISG15: structure, function, and ISG15 conjugation 19

1.5.2 USP18 is a deconjugating enzyme for ISG15 and a negative regulator for

IFN signaling 23

1.6 Model systems to study HCV replication and viral production 24

1.6.1 HCV Replicon system 24

1.6.2 HCV pseudoparticle (HCVpp) system 25

1.6.3 JFH full-life cycle HCV replication and production 26

1.6.4 The chimeric SCID-Alb/uPA mouse model 28

Chapter 2: Search for a response signature in liver tissues of patients chronically

infected with HCV 41

Paper 1. Hepatic Gene Expression Discriminates Responders and

Nonresponders in Treatment of Chronic Hepatitis C Viral Infection 42

Abstract 43

Introduction 44

Material and methods 45

Results 50

Discussion 62

Acknowledgement 64

Chapter 3: Confirmation of the HCV response signature-prospective

validation and cell type-specific expression of ISG proteins identified by

immunohistochemical staining 68

Paper 2: Cell-type specific gene expression signature in liver underlies response

to interferon therapy in chronic hepatitis C infection 69

vii

Abstract 71

Introduction 72

Materials and methods 74

Results 78

Discussion 99

Supplementary data 105

Chapter 4: Microarray screen identifies in an unbiased way the ISG15/USP18

pathway to be involved in IFN resistance in HCV infected patients 113

Chapter 4-1: The role of USP18 in HCV production 113

Paper 3: Silencing of USP18 Potentiates the Antiviral Activity of Interferon

Against Hepatitis C Virus Infection 114

Abstract 115

Introduction 117


Results 122

Discussion 137

Acknowledgement 139

Chapter 4-2: The role of ISG15 in HCV production 143

Paper 4: ISG15, a ubiquitin-like interferon stimulated gene, promotes

Hepatitis C Virus production in vitro: Implications for chronic infection and

response to treatment 144

Summary 146

Introduction 147


Results 152

Discussion 162

Supplementary data 167

viii

Chapter 5: General discussion 172

5.1 Identification of an HCV response signature and its validation 172

5.2 Cell-type specific expression of ISGs underlies treatment response in HCV

infected patients 175

5.3 ISGs with pro-HCV roles 178

5.4 USP18 and its role in HCV infection and viral resistance to IFN therapy 180

Chapter 6: Future work 187

6.1 Identification of ISG15 and USP18 interacting proteins 187

6.2 Understanding the cell-type specific expression of ISG proteins in hepatocytes Vs

macrophages 188

6.3 Is there any link between the IL28B SNP and ISG expression in the liver? 188

ix

List of Tables


infected with HCV

Table 2-1.Real-Time PCR Primers for 18 differentially-expressed genes in the

pretreatment livers of Rs and NRs 48

Table 2-2. Patient Characteristics: All 31 Patients 51

Table 2-3. Eighteen Genes That Differ Between NR and R Hepatic Gene Expression

Profiles 53

Table 2-4. Patient Characteristics: Patients With Genotype 1 Infection Only 60

Chapter 3: Confirmation of the HCV response signature-prospective validation and

cell type-specific expression of ISG proteins identified by immunohistochemical

staining

Table 3-1. Patient demographics 79

Table 3-2: Response signature validation based on gene expression data from cDNA

microarray 81

Table 3-3A: Genes affected by viral genotype (―response to treatment‖ excluded) 85

Table 3-3B: Genes affected by response status 87

Table 3-4: real-time PCR primers 106

Chapter 4-2: The role of ISG15 in HCV production

Table 4-2-1. Real-time RT-PCR primers 167

x

List of Figures

Chapter 1: Introduction

Figure 1-1. HCV genome and encoded structural/non-structural proteins 2

Figure 1-2. Type I IFN induction and the Jak/STAT pathway 6

Figure 1-3. The overall polypeptide fold for ISG15 and its similarity to the ubiquitin fold

20

Figure 1-4. Systems for the study of HCV replication, entry, and infectivity 27


infected with HCV

Figure 2-1. Real-time PCR verification of genes predicted to be altered by microarray 55

Figure 2-2. Hierarchical cluster analysis using the 18 genes present in all 31 samples 56

Figure 2-3. Cluster and classifier analysis using the 8-gene predictor set: all patients 58

Figure 2-4. Cluster and classifier analysis using the 8-gene classifier set: patients with

genotype1 infection only 61

Chapter 3: Confirmation of the HCV response signature-prospective validation and

cell type-specific expression of ISG proteins identified by immunohistochemical

staining

Figure 3-1 Hierachial clustering analysis of 78 HCV chronically infected liver samples

based on expression levels of 18 previously-defined signature genes 82

Figure 3-2. Multivariate ANOVA analysis of genes altered by chronic HCV infection 84

Figure 3-3. Hierarchical cluster plot of CHC genotype 1 patients segregated by the 17

genes specific to ―genotype‖ 89

Figure 3-4: ISG activation in different cell types in the pretreatment livers of R and NR

92

Figure 3-5. ISG15-specific immunohistochemical staining 96

Figure 3-6. Real-time PCR for inflammatory mediators in normal, R and NR livers 107

xi

Chapter 4: Microarray screen identifies in an unbiased way the ISG15/USP18

pathway to be involved in IFN resistance in HCV infected patients

Chapter 4-1: The role of USP18 in HCV production

Figure 4-1-1. Inhibition of USP18 expression by USP18 siRNA 124

Figure 4-1-2. USP18 silencing augments the antiviral effects of IFN against HCV

infection 126

Figure 4-1-3. Multiple USP18 siRNAs enhance the antiviral effects of IFN against HCV

128

Figure 4-1-4. USP18 silencing augments the antiviral effects of IFN in cells previously

infected with HCV 130

Figure 4-1-5. USP18 silencing enhances protein ISGylation and ISG15 induction by IFN

132

Figure 4-1-6. General enhancement of ISG induction in USP18-silenced cells 134

Figure 4-1-7. USP18 silencing prolongs STAT1 activation after IFN treatment 136


Figure 4-2-1. Modulation of ISGylation in Huh7.5 cells 153

Figure 4-2-2. Ube1L mRNA expression was silenced by SiRNA 154

Figure 4-2-3. ISG15 promotes HCV production in vitro 156

Figure 4-2-4. Increased ISG15/Isgylation blunts IFNα anti-HCV activity 157

Figure 4-2-5. Silencing Ube1L inhibits HCV production 158

Figure 4-2-6. Comparison of individual vs pooled Ube1L siRNA 159

Figure 4-2-7. IFNα-induced ISG expression following Ube1L SiRNA knock down 161

xii

List of Abbreviations

cEVR ―complete‖ Early Viral Response

CHC chronic hepatitis C

CTL cytotoxic T-lymphocyte

dsRNA double-stranded RNA

EC50 median effective concentration

EIF2α eukaryotic translation initiation factor 2α

ESCRT-I endosomal complex required for transport-I

ETR end of treatment response

GAPDH glyceraldehyde-3-phosphate dehydrogenase

GWAS genome-wide association study

HCV Hepatitis C Virus

HCVpp retroviral pseudotypes bearing HCV glycoproteins

IFN/RBV IFNa and Ribavirin

IFNs interferons

IRF3 interferon regulatory factor 3

ISGs IFN-stimulated genes

KNN nearest neighbor analysis

LCMV lymphocytic choriomeningitis virus

LDA linear discriminant analysis

mAb monoclonal antibody

Mda5 Melanoma differentiation associated gene 5

MOI multiplicity of infection

NK natural killer

xiii

NPV negative prediction value

NR non-responder

OAS1 2‘5‘-oligoadenylate synthetase 1

pAb polyclonal antibody

PAMP pathogen associated molecular patterns

PCA principal components analysis

PCR polymerase chain reaction

pegIFN/rib pegylated interferon/ribavirin

PKR protein kinase R

PPV positive prediction value

R Responder

RdRP RNA dependent RNA polymerase

RIG-I retinoic-acid inducible gene I

RNaseL ribonuclease L

RVR Rapid Viral Response

siIRR irrelevant small inhibitory RNA

siRNA small inhibitory RNA

SNP single nucleotide polymorphism

SNV Sindbis virus

SR-BI scavenger receptor class B type I

ssRNA single-stranded RNA

STAT-C specific targeted antiviral therapy for hepatitis C

SVR sustained virological response

TLR3 Toll-like receptor 3

Ube1L ubiquitin E1 like

USP ubiquitin-specific protease

USP18 ubiquitin-specific protease 18

VSV vesicular stomatitis virus

xiv

List of Published Papers

Thesis related:

1. Chen L., Ivan Borozan, Jordan Feld, Jing Sun, Laura-Lee Tannis, Catalina Coltescu, Jenny

Heathcot, Aled M. Edwards, Ian D. McGilvray. Hepatic gene expression profiling

discriminates responders and non-responders in treatment of chronic hepatitis C viral

infection . Gastroenterology 2005; 128:1437-1444 (Chapter 2)

2. Chen L, Borozan I, Sun J, Guindi M, Fischer S, Feld J, Anand N, Heathcote J, Edwards

AM, McGilvray ID. Cell-type specific gene expression signature in liver underlies response

to interferon therapy in chronic hepatitis C infection. Gastroenterology 2010;138(3):1123-

1133.e3. Epub 2009 Nov 6. (Chapter 3)

3. Randall G, Chen L, Panis M, Fischer AK, Lindenbach B, Sun J, Heathcote J, Rice CM,

Edwards AM, McGilvray ID. Silencing of USP18 potentiates the antiviral activity of

interferon against hepatitis C virus infection. Gastroenterology 2006; 131(5):1584-91

(Chapter 4)

4. Chen L, Sun J, Meng L, Heathcote J, Edwards A, McGilvray I. ISG15, a ubiquitin-like I

interferon stimulated gene, promotes Hepatitis C Virus production in vitro: Implications for

chronic infection and response to treatment. J Gen Virol. 2010 Feb;91(Pt 2):382-8. Epub

2009 Oct 21.(Chapter 4)

5. Selzner N, Chen L, Borozan I, Edwards A, Heathcote EJ, McGilvray I. Hepatic gene

expression and prediction of therapy response in chronic hepatitis C patients. J Hepatology

2008;48(5):708-13 (Chapter 1)

6. Borozan I, Chen L, Paeper B, Heathcote JE, Edwards AM, Katze M, Zhang Z,McGilvray.

ID. MAID : an effect size based model for microarray data integration across laboratories

and platforms BMC Bioinformatics. 2008;9:305. (Chapter 1)

Thesis non-related:

1. Limin Chen, Ivan Borozan, Piotr Milkiewicz

, Jing Sun

, Xiangbin Meng, Catalina

Coltescu, Aled M Edwards , Mario A Ostrowski, Maha Guindi, E.J Heathcote, Ian D

McGilvray. Gene expression profiling of early primary biliary cirrhosis: Insights into the

http://www.ncbi.nlm.nih.gov/pubmed?term=%22Chen%20L%22%5BAuthor%5D&itool=EntrezSystem2.PEntrez.Pubmed.Pubmed_ResultsPanel.Pubmed_RVAbstract

http://www.ncbi.nlm.nih.gov/pubmed?term=%22Sun%20J%22%5BAuthor%5D&itool=EntrezSystem2.PEntrez.Pubmed.Pubmed_ResultsPanel.Pubmed_RVAbstract

http://www.ncbi.nlm.nih.gov/pubmed?term=%22Meng%20L%22%5BAuthor%5D&itool=EntrezSystem2.PEntrez.Pubmed.Pubmed_ResultsPanel.Pubmed_RVAbstract

http://www.ncbi.nlm.nih.gov/pubmed?term=%22Heathcote%20J%22%5BAuthor%5D&itool=EntrezSystem2.PEntrez.Pubmed.Pubmed_ResultsPanel.Pubmed_RVAbstract

http://www.ncbi.nlm.nih.gov/pubmed?term=%22Edwards%20A%22%5BAuthor%5D&itool=EntrezSystem2.PEntrez.Pubmed.Pubmed_ResultsPanel.Pubmed_RVAbstract

http://www.ncbi.nlm.nih.gov/pubmed?term=%22McGilvray%20I%22%5BAuthor%5D&itool=EntrezSystem2.PEntrez.Pubmed.Pubmed_ResultsPanel.Pubmed_RVAbstract

xv

mechanism of action of ursodeoxycholic acid. Liver International 2008 Aug;28(7):997-

1010

2. Selzner M, Selzner N, Chen L, Borozan I, Sun J, Xue-Zhong M, Zhang J, McGilvray

ID.Exaggerated up-regulation of tumor necrosis factor alpha-dependent apoptosis in the

older mouse liver following reperfusion injury: targeting liver protective strategies to

patient age. Liver Transpl. 2009 Nov;15(11):1594-604.

3. Ivan Borozan* , Limin Chen*, Jing Sun, Laura-Lee Tannis, Maha Guindi, Ori D. Rotstein,

Jenny Heathcote, Aled M. Edwards, David Grant, Ian D. McGilvray

. Gene expression

profiling of acute liver stress during living donor liver transplantation American Journal of

Transplantation 2006 Apr;6(4):806-24 ( * equal contribution)

1

Chapter 1: INTRODUCTION

1.1 HCV : virology, epidemiology and treatment

Hepatitis C virus (HCV) is the only member of the genus Hepacivirus in the family Flaviviridae.

1,2 Identified as the pathogen that caused the non A-non B hepatitis in 1989, HCV infection is

one of the most common causes for liver diseases, currently infecting 170 million people

worldwide.3,4

After exposure to HCV, 60-80% of infected patients develop chronic infection

despite the induction of HCV-specific antibodies and a HCV-specific cellular immune response.

5,6 HCV chronic infection frequently results in progressive fibrosis, cirrhosis and an increased

risk of hepatocellular carcinoma.7 As such, HCV is a significant health burden and the leading

indicator for liver transplantation in the US and Western Europe. 1

The HCV genome consists of a positive strand RNA, 9.6Kb in length, encoding a single

polyprotein that is processed by both host and viral proteases to generate 3 structural (Core, E1,

E2) and 7 non-structural (P7, NS2/3, NS3, NS4A, NS4B, NS5A, and NS5B) proteins (Figure 1-

1).8 There are six major HCV genotypes; genotype 1 is the most prevalent genotype in North

American, Asia, and Europe, while genotype 4 is the most common in Egypt. 9

2

Figure 1-1. HCV genome and encoded structural/non-structural proteins

Adapted from Robert Thimme, et al. Antiviral Research 2006; 69:129-141

The HCV life cycle starts with receptor-mediated viral particle endocytosis. Several receptors or

co-receptors have been identified, including CD81 9, the scavenger receptor class B type I (SR-

BI) 10

, claudin-1 11

, occludin 12

, DC-SIGN and L-SIGN 13

. Once inside the cell, the HCV virion

3

is de-coated and the positive strand RNA is released into the cytoplasm to function as an mRNA

template to direct IRES-mediated translation. This translation generates a single polyprotein

(3011aa) that is co- and post-translationally cleaved into 3 structural and 7 non-structural

proteins by host signal peptidase and viral proteases.8

These viral proteins assemble with the

newly-replicated viral RNA to form new virions to be released out of cells to infect other naïve

cells.

HCV infects only humans and chimpanzees. The clinical course of infection varies, but the

majority of humans infected cannot clear the virus and up to 85% will develop chronic infection

- defined as HCV RNA persistence in the serum for more than 6 months as detected by PCR. No

vaccine is available. The current standard of care antiviral therapy consists of combination

therapy with pegylated IFNa and Ribavirin (IFN/RBV). This treatment is often inadequate.

Although there is a 60-80% response rate in patients infected with genotype 2 and 3, there is only

a 30-50% rate of response for genotype 1. Thus, in North America the majority of patients

chronically infected with Hepatitis C (predominantly Genotype 1) will not respond to treatment

with combination of PegIFN/Ribavirin. The treatment is also expensive – estimated at more than

25 000$ US per patient - and associated with significant side effects over a prolonged period of

treatment (12-24 months).14

The fact that the different genotypes respond dramatically differently to combination therapy

also indicates that viral genotype plays an important role in determining the outcome of infection,

although detailed mechanisms remain poorly understood.

4

1.2 HCV and innate immune response: induction and evasion mechanisms

1.2.1 Innate immune response to HCV infection: Type 1 IFN production and the Jak/STAT

pathway

The first line of host defense to a viral infection is the innate immune response, which promotes

the subsequent adaptive immune response. The innate immune response is characterized by the

production of type 1 interferon (principally IFN and ), IFN-stimulated genes (ISGs) and the

activation of natural killer (NK) cells. NK cells mainly circulate in the blood, where they account

for 5 to 15% of circulating lymphocytes; by contrast, they represent up to 45% of tissue-

infiltrating lymphocytes in the liver.15

The innate immne response is triggered by pathogen

associated molecular patterns (PAMPs) such as double-stranded RNA (dsRNA), single-stranded

RNA (ssRNA) and unmethylated CpG motifs in DNA. IFN is produced after activation of

RIG-I/Mda5 (retinoic acid inducing gene-I/Melanoma differentiation associated gene 5)16,17

or

endosomal Toll-like receptor 3 (TLR3) 18

pathways. After IFNβ is expressed and binds to its

cognate type 1 IFN receptors, IFN and interferon stimulated gene (ISG) production is

stimulated through the Jak/STAT pathway.19

(Figure 1-2) Many ISGs are known to have anti-

viral activity, such as 2‘5‘-OAS1, PKR, MxA and P56. 20

2‘5‘-oligoadenylate synthetase 1 (OAS1) is expressed at low constitutive levels as an inactive

monomer in the cytoplasm and is upregulated by type I interferons (IFNs). Following activation

by viral double-stranded RNA (dsRNA), the enzyme oligomerizes to form a tetramer that

synthesizes 2‘5‘-oligoadenylates that, in turn, activate the constitutively expressed inactive

ribonuclease L (RNaseL), and this then enables RNAseL to cleave cellular (and viral) RNAs. 21

5

Protein kinase R (PKR) accumulates in the nucleus and cytoplasm as an inactive monomer,

which is activated directly by viral RNAs. Following activation, PKR monomers are

phosphorylated and dimerize to form the active enzyme. Activated PKR plays an important role

in viral defence by inhibiting translation through phosphorylation of eukaryotic translation

initiation factor 2α (EIF2α). 22 The MxA protein accumulates in the cytoplasm on intracellular

membranes (such as the endoplasmic reticulum, ER) as oligomers. Following viral infection,

MxA monomers are released and bind viral nucleocapsids or other viral components, to trap and

then degrade them. 23 The P56 families of proteins have been implicated in IFN‘s antiviral

actions against HCV, West Nile virus and LCMV.24,25 The C-terminal region of P56 mediates its

interaction with eIF-3e and causes an impairment of eIF-3 function and resultant inhibition of

protein synthesis.26,27 In addition to function through impairment of eIF-3 and resultant

inhibition of protein synthesis the antiviral effect of P56 was ascribed to its direct interaction

with some viral proteins. For example, in HPV virus infection, P56 interacts with the DNA

replication origin-binding protein E1 of several strains of HPV to directly inhibit HPV

replication 28

The innate immune response also involves activation of NK cells, whose main roles are to

produce immune-regulatory cytokines (IFNγ, TGF-β, IL3, GM-CSF, and TNF ) 29

and to link

the innate and adaptive immune responses by activating dendritic cells (DCs) in TNF -

dependent manner.

6

Figure 1-2. Type 1 IFN induction and the Jak/STAT pathway

Adapted from O. Haller et al. Virology 344 (2006) 119-130

1.2.2 Adaptive immune response to HCV infection: humoral and cell-mediated immune

responses

The adaptive immune response to HCV consists of a B-cell response (HCV-specific antibody)

and a T-cell response (CD4+ T helper cells and CD8+ Cytotoxic T lymphocytes).

7

While anti-HCV antibodies are easily detected in the overwhelming majority of chronic HCV

cases, 30

they are not neutralizing and do not play a major role in determining the outcome of

HCV infection. The lack of a role is demonstrated by the fact that a robust humoral response

does not provide protection against re-infection in chimpanzees and humans, and the levels of

anti-HCV antibodies in patients do not correlate with a more favourable outcome of infection.

31,32 This said, chimpanzees and humans who have cleared the virus seem to be less likely to

develop chronic infections after re-exposure. 33-35

However, there is strong evidence that virus-specific CD4+ and CD8+ T cells (cellular immune

response) play a major role in viral control and clearance – i.e. in determining the outcome of

infection (resolution or persistence). 36,37

Lymphocyte depletion studies in chimpanzees have

demonstrated a crucial role for both CD4+ and CD8+ T cells in mediating protective immunity.

38,39

1.2.3 HCV evasion from innate immune response: counteracting IFN pathways and inhibiting

NK cell activity

Even after successful induction of HCV-specific antibodies and T-cell responses, 60-80% of

infected patients develop chronic infections. 5,6

This fact indicates that HCV has evolved a

number of mechanisms to evade both the host innate immune response and the adaptive immune

response. HCV evades the host innate immune response by inhibiting IFN production, interfering

with IFN signaling transduction (JAK/STAT pathway) and modulating IFN effector molecules.

40 Quite surprisingly, HCV has also developed a mechanism to inhibit NK cell activity: the E2

8

structural protein of HCV crosslinks CD81 on NK cells to inhibit cytotoxicity and IFN

production. 41

These countermeasures appear to be quite efficient, since almost 85% of the HCV-

infected patients develop a chronic infection, and up to 60% of those patients do not respond to

IFN therapy or experience a relapse when therapy is stopped. 42

1.2.4 HCV evasion from adaptive immune response: lack of priming or exhaustation of T cell

response

A lack of primary induction of T cell response and exhaustion of an initially vigorous response

are two important predictors of viral persistence both for HCV and other viruses. 43

Lack of

priming may be due to impairment of antigen presentation by DCs and macrophages. 44

For

example, Kaposi sarcoma herpes virus (KSHV) encodes a membrane-bound viral E3 ligase (K3

or K5) that promotes MHC class 1 molecule polyubiquitination and degradation in lysosomes.45

T cell exhaustion might also result from deletion of virus-specific T cells in the presence of

continuously high viral load or lack of a functional CD4+ T helper cell response. 46

Most

recently, programmed death 1 (PD-1) protein, of the CD28 family of receptors, was shown to be

a marker for these exhausted T cells. 47

Binding of PD-1 to one of its ligands, PD-L1 or PD-L2,

transmits a negative signal to the T cells expressing PD-1, reducing cytokine production such as

interleukin-2, tumor necrosis factor-α and interferon-γ and proliferation. 48

Quite interestingly,

high levels of PD-1 expression were found on bulk CD8+ T cells and intrahepatic HCV-specific

CD8+ T cells from the livers of patients with chronic HCV infection 49

, with higher expression

level of PD-1 on liver infiltrating HCV-specific CD8+ T cells compared with peripheral blood

50. As in HIV infections

51,52, in vitro blockade of PD-1/PD-1L has been shown to improve the

functionality of the impaired HCV-specific CD8+ T cells from the peripheral blood. 53

9

HCV virus mutational escape might also play a role in evasion. For example, mutations in the

hypervariable region of E2, which is the antibody recognizing site, may allow the virus to avoid

antibody neutralization.54

The virus in the liver of HCV infected patients replicates very rapidly

with a viral half-life (the time taken for half of the viruses to be cleared) about 3 hours, and about

1012

virions are produced per day in an infected person.55

Rapid replication in conjugation with

the lack of proof-read ability of the HCV RNA dependent RNA polymerase (RdRP) makes the

virus exist as a quasispecies in patients. These diversities of viral genomes favour the virus

evading host immune attack. 56

1.3 Predicting treatment response: current approaches and limitations

As noted earlier, a considerable number of persons chronically infected with HCV are subjected

to a highly morbid, highly costly treatment involving the combination of PegIFN and ribavirin.

At present there is no reliable way to accurately predict treatment responses prior to initiation of

therapy. Much effort has been made to determine which patients will respond to treatment as

soon as possible.57-59

The current standard is to measure decreases in viral load early during

treatment. Patients with a Rapid Viral Response (RVR) - defined as having no detectable virus

at 4 weeks of treatment – have an 88% chance of being cured. Unfortunately, only 19% of

genotype 1 patients achieve an RVR. Patients with a ―complete‖ Early Viral Response (cEVR) –

no detectable virus at 12 weeks of treatment – have a 68% chance of being cured; however, <70%

of genotype 1 patients achieve an EVR. The negative predictive value is only about 50% for

both.60

In addition, these measures can only be implemented after treatment has been initiated,

10

which is sub-optimal: in clinical practice, patients are often unwilling to start treatment due to the

side-effects and the low probability of success. An ability to predict treatment response prior to

initiating treatment would encourage patients to start and to continue treatment.

Although a few specific targeted antiviral therapies for hepatitis C (STAT-C), such as NS3/4A

protease and NS5B polymerase inhibitors, are being developed with the most promising one

entering into phase III clinical trial, viral mutations that are resistant to these modalities often

occur. 61

As a result, it is quite likely that Pegylated IFN/Ribavirin will remain a mainstay of

therapy for the foreseeable future, to be administered alone or in combination with

protease/polymerase inhibitors. 61

1.4 Pre-treatment predictors of response in HCV: genomics-based approaches

In addition to viral and patient characteristics, the genetic diversity of the host contributes to the

outcome of infection and treatment of chronic HCV. High-throughput techniques now allow for

the rapid and accurate characterization of gene expression in tissues, and for the detection of

individual host genetic polymorphisms.

1.4.1 Hepatic gene expression

Gene expression profiling studies that have looked at the effects of HCV infection in the host

liver have often aimed to associate changes in the expression of individual genes with clinical

outcomes or treatment responses. For example, I used a 19,000 cDNA microarray to study pre-

treatment liver biopsy specimens taken from patients with chronic HCV who were subsequently

11

treated with combination therapy with PegIFNα/RBV. 62

Gene expression levels were compared

among 15 non-responder, 16 responder, and 20 normal liver biopsy specimens. I identified a

discrete set of 18 genes whose expression differed consistently between responders and non-

responders (p < 005). 62

Many of these 18 genes were IFN sensitive genes (ISGs), and three of

them (USP18/UBP43, CEB1, and ISG15) play roles in the same IFN regulatory pathway,

suggesting a possible rationale for treatment resistance. These results have now been

substantiated by other groups 63,64

, and have been validated prospectively in a larger cohort of

chronic HCV patients prior to initiating antiviral treatment. 65

Using four different methods for

classification accuracy (KNN, DQDA, DLDA, CART), we demonstrated that the 18 gene

signature has a positive prediction value (PPV) of at least 95% for prediction of treatment

response, though a negative prediction value (NPV) of only 50% – a predictive capacity similar

to the RVR (Rapid Virological Response is defined as having more than 2 log10 viral titer drop

at 4 weeks post treatment) and 48 hour drop in viral titers above. We went on to confirm that the

USP18/UBP43 protease, a down-regulator of Type I interferon responses identified in our initial

microarray experiment, plays an important role in regulating the anti-HCV effect of IFNα. 66

In another array study by Hayashida et al 67

, they analyzed liver tissue samples obtained prior to

the treatment of 69 HCV patients who then received either IFNα monotherapy or IFNα/RBV. Of

these 69 samples, 31 were used as a training set to develop an algorithm for predicting interferon

efficacy and 38 were used to validate the algorithm. They also applied their methodology to the

prediction of the efficacy of IFNα/RBV combination therapy using an additional 56 biopsies. For

the IFN group, genes differentially expressed were mainly IFN, lipid metabolism, complement,

12

and oxidoreductase-related genes. For the IFNα/RBV combination group a different set of genes

was identified, including cyclophilin A and multidrug resistance protein with an accuracy of 93%

for prediction of SVR/non-responders. The pattern of the genes in this study is different from

ours and others, possibly because the majority of the patients (69%) in this study were genotype

2, which have a significantly higher SVR. It would be interesting to reanalyze these data after

excluding genotypes 2 patients.

Asselah et al. recently identified a two-gene signature (IFI27 and CXCL9) that accurately

predicted treatment response in 79% of patients being treated for chronic HCV. 64

Using a large

scale real-time quantitative RT-PCR to analyze the mRNA expression of 58 selected genes in

liver biopsies of treatment-naive HCV patients, they found that the two-gene signature had a

predictive accuracy of 100%, 70%, and 73% in non-responders, sustained responders and

responder-relapsers. The predictive values of these genes also held true when the authors sub-

analyzed patients according to both genotype 1 and to the severity of fibrosis. Both IF127 and

CXCL9 belong to the interferon-stimulated gene (ISG) family, and both are up-regulated in the

pre-treatment liver tissue of patients who do not respond to treatment.

Feld et al. used a microarray-based approach to shed light on the mechanisms of action of

combination PegIFNα/RBV therapy. 63

They extended our previous observation that ISGs are

more highly expressed in the pre-treatment liver tissue of non-responders than responders. In this

study patients were randomized either to receive or not to receive RBV. All patients received

13

IFN 24 h prior to liver biopsy; those randomized to the RBV arm were treated with this drug for

72 h prior to the biopsy. The combination of PegIFNα/RBV resulted in greater up-regulation of

genes involved in the interferon signaling cascade, and a more pronounced down-regulation of

genes involved in IFN-inhibitory pathways, than did monotherapy with IFN. Additionally, pre-

treatment ISG expression seemed to be higher in the liver tissue from slow responders than in the

liver tissue of rapid responders. During treatment rapid responders had a higher fold induction of

ISGs. A major puzzle is why the pre-treatment up-regulation of ISGs in the livers of non-

responders is not able to eliminate the virus, whereas the IFN-driven up-regulation of ISGs in

responders is associated with viral control. One possibility is that the virus in non-responders

has had an opportunity to adapt mutationally to the up-regulation of ISGs.

Taken together, these studies argue that discrete gene subsets have predictive value in the

treatment of chronic HCV infection, and offer intriguing insights into the mechanisms

underlying treatment response and non-response.

1.4.2. Gene expression in blood

Given that HCV replicates almost exclusively in the liver, it is likely that the strongest HCV

―signal‖ would be found in liver tissue. However, there is no question that it would be more

convenient to develop a predictive test from blood. A number of groups have tried and generally

failed to find a gene expression signature in peripheral blood that correlates with treatment

outcomes in chronic HCV. One recent study was able to correlate peripheral blood gene

14

signatures with treatment responses – possibly because they focused on patients co-infected with

both HCV and HIV. The study analyzed gene expression profiles in peripheral blood

mononuclear cells (PBMCs) to predict treatment response in patients co-infected with HCV and

HIV. 68

The authors used a class prediction analysis of gene expression patterns in the PBMCs of

29 patients prior to antiviral treatment in order to predict the response of the patients to

combination therapy. Seventy-nine genes correctly classified all 10 patients who did not respond

to therapy, 8 of 10 patients with end of treatment response (ETR), and 7 of 9 patients with SVR.

The same analysis was performed after therapy was initiated to predict SVR among patients with

an EVR. Prediction analysis of the 17 post-treatment samples identified 105 genes that correctly

identified all 9 patients with ETR and 7 of 8 patients with SVR. As with the intrahepatic profiles,

failure of antiviral therapy was associated with increased expression of ISGs prior to treatment

and the inability of these genes to be further stimulated by IFN administration. With the caveat

that these results were generated in co-infected patients, overall the findings are consistent with

the idea that dysregulation of subsets of IFN-stimulated genes in chronic HCV may be a

biomarker of immune dysfunction and non-response to IFN plus RBV. These studies also

suggest that non-responders tend to have high ISG expression pre-treatment, which is consistent

with our previous findings from gene expression profiling, and are not able to increase ISGs

much following initiation of treatment. 62,69

The basis for this response is unclear.

Gerotto et al. studied the role of IFN-inducible protein kinase (PKR) in PBMCs and liver

biopsies of patients with chronic HCV. They demonstrated that non-responders to combination

therapy had pre-treatment mRNA levels in PBMCs and in liver that were significantly higher

15

than the responders. However, no difference in PKR mRNA levels were found in PMBCs of

responders compared to the non-responders after in vitro exposure to IFN. Taken together these

results indicate an endogenous activation of IFN production in non-responders prior to antiviral

therapy. 70

Taylor et al. studied gene expression profiles in PMBC samples from a group of patients infected

with HCV genotype 1 during the first 28 days of IFN-based combination therapy. Results were

analyzed with respect to treatment response (poor viral response – <1.5 log 10 IU/ml decrease of

HCV RNA at day 28 – compared to marked viral response – >3.5 log 10 IU/ml decrease) and to

race (African-American vs. Caucasian). They demonstrated that patients with a marked viral

response had pronounced changes in PBMC gene expression, while patients with a poor viral

response did not. ISG expression was strongly altered, suggesting that poor response to IFN-

based therapy may be due to blunted induction of interferon responsive gene expression.

Whether this lower response is determined by host genetics or due to the environment is unclear;

and the results should be approached with caution given that they were generated from peripheral

immune cells. 71

1.4.3. Host genetic polymorphisms

Single nucleotide polymorphisms (SNPs) are the most common form of genetic variation, with

more than nine million reported in public databases. 72

A SNP is a single nucleotide variation at

a specific location in the genome that is by definition found in more than 1% of the population. 73

16

In general, SNPs occur much less frequently in coding regions of the genome than in non-coding

regions, with most SNPs being located in non-coding regions. 74

SNPs in non-coding regions,

although they do not alter encoded proteins, can be useful as physical markers for comparative or

evolutionary genomics studies. In the coding regions, SNPs can cause alterations in protein

structure and hence function, leading to the development of disease or change in response to a

drug or environmental toxin.

In several recent studies of patients with chronic HCV, SNP analysis for candidate genes was

employed to predict both disease progression75

and therapeutic response.76,77

Genes that were

assessed for disease-associated SNPs included interferon-stimulated genes such as 2–5

oligoadenylate synthetase (2–5 OAS), dsRNA activated protein kinase (PKR) and MxA. These

genes are of special interest since the anti-HCV activity of IFN is likely mediated, at least in part,

by their induction. Several studies have reported an association with SNPs in the promoter

regions of MxA, OAS-1 and PKR .76,78,79

While it is difficult to draw firm conclusions from

these mostly small studies, it is interesting that both OAS and MxA were found to be associated

with treatment response in our impartial gene expression profiling study.

Other host factors that might also determine the response to treatment, such as cytokines,

chemokines and their receptors, have also been examined for SNP associations. 77,80,81

Among

the cytokines examined was IFN-γ, which is produced by effector T and natural killer cells and

has a critical role in host defense against a variety of intracellular pathogens, including HCV.

17

IFN-γ efficiently inhibits HCV replication in the replicon system in vitro 82

and the intrahepatic

level of IFN-γ appears to be associated with viral clearance in the chimpanzee model.83

In a

recent study of two patient cohorts, Huang et al. demonstrated an important role between one

specific polymorphism of IFN-γ and treatment response.84

The first cohort included 280 chronic

HCV patients who had received INFα-based therapy. The second cohort contained 250 IV drug

abusers who either spontaneously cleared the virus or became chronically infected. The authors

demonstrated that among the eight analyzed SNPs spanning the entire IFN-γ gene in the two

cohorts, only one SNP variant (−764G, located in the proximal I IFN-γ promoter region) was

significantly associated with sustained virological response in the first cohort and with

spontaneous recovery in the second cohort. Other cytokines such as IL10 have been studied in a

similar manner. Yee et al. reported that two SNP variants of IL10 (−592A and −819T SNP)

were more frequent among the patients with SVR compared to the non-responders to treatment

with IFN/RBV.85

Further gene-specific and genome-wide association studies are needed to link

genetic polymorphisms with clinical or treatment outcomes.

Most recently, four groups, using genome-wide association study (GWAS), reported that a

single-nucleotide change at roughly 3kb upstream of the promoter region of IL28B gene (which

encodes a type III IFNλ3) is associated with both treatment-induced and spontaneous Hepatitis C

Virus clearance. 86-89

Ge et al. scanned the genomic DNA sequences from more than 1600

treatment-naïve HCV genotype 1 (G1) infected patients and found that SNP rs12979860 was

strongly associated with treatment induced SVR. 89

This SNP was also found to be associated

with virus spontaneous clearance. 86

18

It is of note that, although SNP rs12979860 is indeed the strongest hit that was found to be

associated with treatment-induced or spontaneous HCV clearance from these GWAS, the authors

did find that other SNPs were also associated, albeit the associations were statistically weaker.

However, from the top 100 SNPs that were associated with SVR, none of them replicated

previous reported SNPs ( MxA, OAS-1, PKR, IFNγ, and IL-10) that were associated with

treatment response.89

Failure to replicate these previous findings suggests that either those

associations were too weak to be picked up by GWAS or those previous SVR associated SNPs

were not real. To support this notion, from 637 non-Hispanic Caucasian patients infected with

genotype 1, a study by Morgan et al. indicated that none of the 8 previously reported treatment

response-associated SNPs was associated with SVR.90

Taking all these together, further cross-

validation using even larger patient populations for GWAS is needed to confirm these

associations.

1.5 ISG15 and USP18 in viral disease

The ISGs identified in our microarray study suggested a possible mechanism for treatment

nonresponse. Three of the genes that are overexpressed in non-responders - interferon stimulated

gene 15 (ISG15), ubiquitin specific protease 18 (USP18/UBP43), and CEB1/Herc5 (a HECT

domain ISG15 E3 ligase) - are linked to a ubiquitin-like protein (Ubl)/ ubiquitin specific protease

(ISG15/USP18) pathway.

19

1.5.1 ISG15: structure, function, and ISG15 conjugation

Type I IFNs (IFNα, IFNβ, IFNω) are a group of cytokines that have anti-proliferation, anti-viral

and immunomodulatory activities.76

Initiated by the sensor molecules RIG-I and TLR3, a signal

cascade induced following viral or bacterial infection leads to the production of IFNβ, which is

secreted and binds to the type I IFN receptors (IFNAR) on the surface of target cells to activate

the JAK/STAT signaling pathway. As a result, a few hundred IFN stimulated genes (ISGs) are

induced. Although some of these ISGs have direct anti-viral activity 91-93

, the functions of most

of these ISGs remain unknown.

ISG15 is one of the most abudantly expressed genes induced by viral/bacterial infections or type

I IFN treatment. As the first identified ubiquitin-like protein, ISG15 shares sequence and

structural similarity with ubiquitin (Figure 1-3).

20

Figure 1-3.

The overall polypeptide fold for ISG15 and its similarity to the ubiquitin fold. A, ribbon diagram of ISG15 showing two separate domains, with

color ramped from blue (N terminus, N) to red (C terminus, C) through green (Hinge). The two ubiquitin-like domains (each in β-grasp fold) are

oriented differently and connected by a hinge. The last four C-terminal residues of ISG15 are disordered and not resolved, indicating the

flexibility of the C-terminal tail. B, overlay of ribbon diagrams for ubiquitin (pink) with the amino- (blue) and C-terminal (green) domains of

ISG15 to emphasize the marked similarities in their respective β-grasp folds.

The overall polypeptide fold for ISG15 and its similarity with the ubiquitin fold

Narasimhan J et al. J. Biol. Chem. 2005;280:27356-27365

21

ISG15, like ubiquitin, conjugates to its cellular targets through a series of enzymatic steps.

Conjugation involves first an E1 activating enzyme (Ube1L), 94

then an E2 conjugating enzyme

(UbcH8, UbcH6)95,96

, and finally an E3 ligase (EFP, CEB1/Herc5) .97,98

The C-terminal

LRLRGG sequence of ISG15 is required for conjugation to the lysine residues of target proteins.

ISG15 can be stripped from its target proteins by the USP18 isopeptidase.99

Unlike ubiquitin, conjugation of ISG15 to its target proteins does not usually cause them to be

degraded. Instead, ISG15 conjugation may alter the subcellular localization, structure, stability or

activity of targeted proteins.100

A few hundred proteins with diverse functions in the cellular

skeleton, stress response, immune response, and chromatin remodeling have been identified as

ISG15 conjugation targets. Of particular importance are proteins that play an important role in

innate antiviral response, such as PKR, MxA, Stat1, Jak1, and RIG-I.101,102

Although the

functional consequences of ISGylation, the process of ISG15 conjugation to its targets, are not

known, ISGylation has been implicated in various cellular processes and functions, such as

modulating IFN signaling, antiviral activity, pregnancy, and some forms of cancers.103

Several

lines of evidence suggest that ISG15 conjugation also plays an important role in the innate

antiviral response104

: 1) ISG15 is targeted by other viruses: non-structural protein (NS-1B) of

Influenza B virus binds to the free form ISG15, preventing ISGylation94

; 2) ISG15 inhibits HIV

release (but not virus replication)105

; 3) over-expression of ISG15 in IFN- / receptor knockout

mice protected them against Sindbis virus-induced lethality and decreased Sindbis virus

replication in multiple organs106

; 4) Mice lacking ISG15 deconjugation enzyme (USP18/UBP43),

resulting in increased ISGylation, are resistant to LCMV and VSV infection.104

22

Two possible mechanisms for an ISG15 antiviral activity have been proposed. First, ISG15 may

conjugate to key cellular proteins or viral proteins and inhibit virus replication. For example, in

HIV infection, the Gag protein is ubiquitinated in order to recruit the endosomal complex

required for transport (ESCRT-I) to the plasma membrane for viral budding. ISG15 has been

shown to conjugate with Gag and thus might prevent Gag from being ubiquitinated. As a

consequence, HIV release would be inhibited.105

Conjugation of ISG15 to interferon regulatory

factor 3 (IRF3), a key signal-transducing factor in the activation of the antiviral innate immune

response, protects IRF3 from ubiquitin-mediated degradation by the 26S proteasome.106

Second,

ISG15 may also act alone, as a cytokine. The free form of ISG15 has been shown to activate

natural killer (NK) and cytotoxic T-lymphocytes (CTL) and to stimulate IFN- production.107

This in turn induced dendritic cell maturation and neutrophil recruitment.

Although it was initially suggested that ISGylation plays an important role in the regulation of

the JAK-STAT pathway and IFN signaling 99,108,109

, IFN signaling is intact in ISG15 knock-out

mice. 110

Furthermore, in vivo analyses of ISG15 antiviral activity report contradictory findings.

Although the replication of the Sindbis virus-expressing ISG15 was inhibited in IFNAR1

deficient mice111

and ISG15 null mice have an increased susceptibility to Sindbis, Influenza, and

HSV-1 virus infections112

, there was no difference in the replication of VSV and LCMV in

ISG15 null and wild type mice.110,113

These data suggest that the antiviral activity of ISG15

might be virus-specific. More recently, ISG15 was shown to be able to negatively regulate IFN

signaling by targeting RIG-I.114

Quite surprisingly, we and others have found that ISG15

promotes viral production in a cell culture model. 115

23

1.5.2 USP18 is a deconjugating enzyme for ISG15 and a negative regulator for IFN

signaling

ISG15 conjugation is reversible and controlled by USP18 (UBP43), an IFN-inducible cysteine

protease of the ubiquitin-specific protease (USP) family.99

USP18 appears to counteract the

effects of interferon; lack of USP18 results in enhanced and prolonged STAT1 phosphorylation,

DNA binding, and increased induction of hundreds of ISGs.116

Perhaps as a result of increased

IFN signaling and effect, USP18 knock out mice show greater resistance to the cytopathic effects

of a number of viruses, including lymphocytic choriomeningitis virus (LCMV), vesicular

stomatitis virus (VSV), and Sindbis virus (SNV).104

USP18-deficient cells exhibit high levels of

ISG15 modified proteins (ISGylation). Furthermore, they are hypersensitive to type I IFN and

undergo apoptosis upon IFN stimulation.108

Thus, USP18 appears to be a negative regulator of

IFN signaling.

Although ISG15 may play a role in the anti-HCV response, the ability of USP18 to regulate the

anti-HCV interferon response may be independent of its ability to deconjugate ISG15. Ablation

of ISG15 110

or its E1 activating enzyme Ube1L in mice117

did not reverse the phenotype of the

USP18 knockout, nor affect IFN-induced JAK/STAT signaling, indicating that neither ISG15

nor ISGylation is essential in JAK/STAT signaling. It was recently reported that USP18

negatively regulates JAK–STAT signaling independently of its isopeptidase activity.118

In that

study, USP18 action was specific to type I IFN responses and achieved through a direct

interaction between USP18 and the IFNAR2 subunit of the type 1 IFN receptor. Binding of

exogenous and endogenous USP18 to IFNAR2 in vivo interfered with the JAK-receptor

24

interaction and led to inhibition of the downstream phosphorylation cascade and other signaling

events. Whether this is a cell- or species-specific mechanism remains to be determined.

Most recently, USP18 was found to modulate the expression levels of the EGF surface receptor

at the protein level. Silencing USP18 by specific siRNA resulted in decreased epithelial growth

factor receptor (EGFR) expression by 50-80% while overexpression of USP18 stimulated EGFR

protein translation in an USP18 protease activity-dependent manner. 119

1.6 Model systems to study HCV replication and viral production

Until recently, HCV research has been hampered by the lack of an appropriate model system.

The HCV cDNA clone was first sequenced in 1989 120

, but it was not until 1997, after the

discovery that the original HCV cDNA clone lacked a highly conserved 3‘-terminal genome

fragment, that the first functional full-length cDNA clones were reported through injection of

this in vitro synthesized HCV RNA into chimpazees. At that time, no RNA replication could be

achieved in cell culture. 121

1.6.1 HCV Replicon system

In 1999, Lohmann et al reported the first cell culture system that allowed HCV RNA

replication.122

This replicon system was based on a subgenomic genotybe 1b (isolate Con1) that

replicates in a human hepatoma cell line (Huh7). As shown in Figure 1-4A, bicistronic replicon

RNAs, encoding a selectable marker (Neor) under control of the HCV IRES in the first cistron

and the HCV replicase proteins (NS3-NS5B) under control of a heterologous IRES from

25

encephalomyocarditis virus in the second cistron, were electroporated into Huh-7-based cell lines.

Replication of these RNAs leads to production of a selectable marker, which allows for selection

of colonies containing active RNA replication.

The replication efficiency of HCV RNA in the early replicon system was very low, which

spurred efforts to optimize the system. The first optimization derived from the discovery of cell

lines that were more permissive to HCV replication. Blight et al. isolated more permissive

subclones of replicon-transduced Huh7 cells cured by alpha interferon treatment.123

Of those was

Huh7.5 subclone, which is deficient in the retinoic-acid inducible gene I (RIG-I) antiviral

response signaling pathway.124

The replicon system was further improved by selecting for

adaptive mutations that dramatically enhanced HCV RNA replication125,126

However, although

robust HCV RNA replication in cultured permissive cells was achieved for both genotype 1b and

1a 18

, there was no infectious virus produced from this system .127-129

1.6.2 HCV pseudoparticle (HCVpp) system

In the process of searching for a system that reproduced the HCV full lifecycle, retroviral

pseudotypes bearing HCV glycoproteins (HCVpp) were generated. 130,131

HCVpp could be

produced by cotransfection of 293T cells with expression vectors encoding (i) HCV E1E2, (ii)

the Gag-Pol proteins of either murine leukemia virus or human immunodeficiency virus, and (iii)

a retroviral genome encoding a reporter to detect subsequent productive entry (Fig. 1-4B). This

HCVpp system is particularly useful to study virus entry.

26

1.6.3 JFH full-life cycle HCV replication and production

In order to develop a system that allows both HCV RNA replication and viral particle production,

it was necessary to understand the mechanisms by which high-level HCV RNA replication was

achieved in cell culture (HCV replicon system), yet no infectious virus was produced. It was

possible that adaptive mutations result in hypophosphorylation of NS5A, which is essential for

HCV RNA replication. 125,132,133

NS5A hypophosphorylation maintains the HCV replicase

stability (allows HCV RNA replication) while sacrificing the HCV late viral life cycle events,

such as assembly and packaging. In support of this idea, most genomes with adaptive mutations

do not produce infectious particles in cell culture despite efficient replication. 127-129

All these

studies led to the hypothesis that a HCV clone capable of replicating in cell culture without

adaptive mutations might yield infectious virus. Such an HCV isolate (JHF-1), isolated from a

Japanese patient with a rare case of acute fulminant hepatitis, was indeed identified.134-136

When

full length JFH-1 RNA is transfected into Huh7 cells, infectious virus particles (HCVcc) are

produced (Figure 1-4C). 137

Higher titers could be obtained in Huh7.5 cells and derived sublines.

123,124 HCVcc is infectious in chimpanzees and uPA-SCID mice transplanted with human

hepatocytes. 138,139

27

Figure 1-4. Systems for the study of HCV replication, entry, and infectivity

(A) HCV replicon systems, shown here in one of their simplest iterations, allow for productive viral RNA replication in cell culture.

Bicistronic replicon RNAs, encoding a selectable marker (Neor) under control of the HCV IRES in the first cistron and the HCV

replicase proteins (NS3-NS5B) under control of a heterologous IRES from encephalomyocarditis virus in the second cistron, are

delivered to Huh-7-based cell lines by electroporation. Replication of these RNAs leads to production of the selectable marker and

allows for selection of colonies containing active RNA replication. Transduction of resistance to the drug G418 is shown in this figure,

but replicons expressing a number of reporter genes have been developed, as have methods to efficiently measure HCV proteins and

RNA from these systems. (B) The HCV pseudoparticle system (HCVpp) provides a method to investigate glycoprotein-mediated

events in the HCV life cycle. In this system, recombinant retroviruses that contain HCV functional glycoproteins on their surface are

generated in 293T cells. These particles can be used to infect permissive cell lines, such as Huh-7.5. The retrovirus genomes have

been engineered to express a reporter gene, such as luciferase, allowing for a quantitative measure of cell entry. (C) The HCVcc

infectious virus system uses either JFH-1 HCV genomic RNA or chimeras of this genome with heterologous sequences (such as J6).

These RNAs are electroporated into permissive cell lines and yield infectious HCV virions that can be used to infect naı¨ve cells or

animal models. Productive infection can be monitored by detection of the expression of NS5A, by a number of reporter genes, or by

direct measure of viral RNA.

Adapted from Tellinghuisen, et al., J Virology 2007; 81(17):8853-8867

28

1.6.4 The chimeric SCID-Alb/uPA mouse model

HCV research had also been hindered by a lack of a small animal model system before the

development of the SCID-Alb/uPA mouse. 140

In this animal, progressive depletion of the mouse

hepatocytes occurs in severe combined immune deficiency (SCID) mice carrying a plasminogen

activator transgene (Alb-uPA). Human hepatocytes can then be transplanted into these mice to

engraft and regenerate the liver. The resulting chimeric human-mouse liver can then be infected

with HCV or HBV. Although this animal model cannot be used to study the immune response

following HCV infection- a critical deficiency- this system is the only small animal model that

supports a full viral life cycle and virion production. It has been very useful in studying virus-

host interactions, as well as in antiviral screening. It is also valuable for distinguishing direct

virus-mediated transcriptional changes from immune-mediated effects, which is impossible to

achieve by studying patient liver biopsies.

In summary, the fact that only half of the patients infected with HCV respond to the current best

regime of treatment with pegylated interferon and ribavirin highlights several major issues in the

HCV research field that need to be solved:

First, it is important to understand why some patients respond to treatment while others do not.

One focus of my PhD research was to understand the molecular mechanism of interferon

resistance in those patients who do not respond to therapy. This will help to develop new drug

targets for better treatment of HCV infected patients.

Second, it is important to develop a prognostic test to predict which patients would respond to

treatment. The other focus of my PhD research was to identify a set of biomarkers (gene

29

expression patterns in HCV-infected pretreatment livers) that might be used to predict treatment

response prior to initiation of therapy.

Third, it is important to develop more specific and effective therapeutics.

Last, the key to eradicating the disease is to develop a vaccine.

30

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http://www.ncbi.nlm.nih.gov/pubmed?term=%22Morgan%20TR%22%5BAuthor%5D&itool=EntrezSystem2.PEntrez.Pubmed.Pubmed_ResultsPanel.Pubmed_RVAbstract

http://www.ncbi.nlm.nih.gov/pubmed?term=%22Lambrecht%20RW%22%5BAuthor%5D&itool=EntrezSystem2.PEntrez.Pubmed.Pubmed_ResultsPanel.Pubmed_RVAbstract

http://www.ncbi.nlm.nih.gov/pubmed?term=%22Bonkovsky%20HL%22%5BAuthor%5D&itool=EntrezSystem2.PEntrez.Pubmed.Pubmed_ResultsPanel.Pubmed_RVAbstract

http://www.ncbi.nlm.nih.gov/pubmed?term=%22Chung%20RT%22%5BAuthor%5D&itool=EntrezSystem2.PEntrez.Pubmed.Pubmed_ResultsPanel.Pubmed_RVAbstract

http://www.ncbi.nlm.nih.gov/pubmed?term=%22Naishadham%20D%22%5BAuthor%5D&itool=EntrezSystem2.PEntrez.Pubmed.Pubmed_ResultsPanel.Pubmed_RVAbstract

http://www.ncbi.nlm.nih.gov/pubmed?term=%22Sterling%20RK%22%5BAuthor%5D&itool=EntrezSystem2.PEntrez.Pubmed.Pubmed_ResultsPanel.Pubmed_RVAbstract

http://www.ncbi.nlm.nih.gov/pubmed?term=%22Fontana%20RJ%22%5BAuthor%5D&itool=EntrezSystem2.PEntrez.Pubmed.Pubmed_ResultsPanel.Pubmed_RVAbstract

http://www.ncbi.nlm.nih.gov/pubmed?term=%22Lee%20WM%22%5BAuthor%5D&itool=EntrezSystem2.PEntrez.Pubmed.Pubmed_ResultsPanel.Pubmed_RVAbstract

http://www.ncbi.nlm.nih.gov/pubmed?term=%22Ghany%20MG%22%5BAuthor%5D&itool=EntrezSystem2.PEntrez.Pubmed.Pubmed_ResultsPanel.Pubmed_RVAbstract

http://www.ncbi.nlm.nih.gov/pubmed?term=%22Wright%20EC%22%5BAuthor%5D&itool=EntrezSystem2.PEntrez.Pubmed.Pubmed_ResultsPanel.Pubmed_RVAbstract

http://www.ncbi.nlm.nih.gov/pubmed?term=%22O'Brien%20TR%22%5BAuthor%5D&itool=EntrezSystem2.PEntrez.Pubmed.Pubmed_ResultsPanel.Pubmed_RVAbstract

http://www.ncbi.nlm.nih.gov/pubmed?term=%22HALT-C%20Trial%20Group%22%5BCorporate%20Author%5D&itool=EntrezSystem2.PEntrez.Pubmed.Pubmed_ResultsPanel.Pubmed_RVAbstract

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41

Chapter 2 Search for a response signature in liver tissues of patients chronically infected with

HCV

Combination therapy of Pegylated IFN and Ribavirin for the treatment of patients chronically

infected with HCV is only effective in 50% of the patients, and there is no reliable method to

predict responsiveness in these patients before initiation of therapy. In this chapter, I describe

how I identified differentially expressed genes (a response signature) from pre-treatment

liver biopsies of patients chronically infected with HCV using cDNA microarrrays. In a

retrospective study, I divided patients into two groups: those who respond to pegylated

IFN/Ribavirin treatment (Responder, R) and those who do not (non-responders, NR). All

these patients had their liver biopsies done before initiation of treatment and the samples

were stored in the RNA stabilization solution (RNAlater, Qiagen) in a -20oC freezer until

used for RNA extraction. 18 genes whose expression levels are constantly and statistically

different between R and NR were identified from 19,000 clones on the microarray chip.

Based on expression levels of these 18 genes, or even 8 genes, I could correctly classify 30

out of 31 patients studied.

This piece of work entitled ―Hepatic Gene Expression Discriminates Responders and

Nonresponders in Treatment of Chronic Hepatitis C Viral Infection‖ was published in

Gastroenterology 2005;128:1437-1444

My role in this paper: experimental design, clinical sample collection, performing the

experiments, summarizing/analyzing data and writing up the paper

42

Hepatic Gene Expression Discriminates Responders and Nonresponders in Treatment of

Chronic Hepatitis C Viral Infection

Limin Chen , Ivan Borozan , Jordan Feld‡, Jing Sun , Laura-Lee Tannis , Catalina

Coltescu‡, Jenny Heathcote

‡, Aled M. Edwards

, §, ¶ and Ian D. McGilvray

,

Banting and Best Department of Medical Research, University of Toronto, Toronto, Ontario,

Canada

‡Department of Medicine, University of Toronto, Toronto, Ontario, Canada

§Department of Medical Genetics and Microbiology, University of Toronto, Toronto, Ontario,

Canada

¶Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada

Department of Surgery, University of Toronto, Toronto, Ontario, Canada

Published in Gastroenterology 2005;128:1437-1444

Reprinted from Gastroenterology, Vol 128, Chen, et al. Hepatic Gene Expression Discriminates

Responders and Nonresponders in Treatment of Chronic Hepatitis C Viral Infection, Page 1437-

1444, copyright (2005), with permission from Elsevier

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ABSTRACT

Background & Aims: Pegylated interferon (IFN)-α plus ribavirin is the most effective treatment

of chronic hepatitis C but has unpleasant side effects and high costs. A large proportion of

patients do not respond to therapy for reasons that are unclear. We used gene expression

profiling to investigate the molecular basis for treatment failure. Methods: Expression profiling

was performed on percutaneous needle liver biopsy specimens taken before therapy. Gene

expression levels were compared among 15 nonresponder, 16 responder, and 20 normal liver

biopsy specimens. Differential gene expression was confirmed using real-time polymerase chain

reaction. Results: We identified 18 genes whose expression differed significantly between all

responders and all nonresponders (P < .005). Many of these 18 genes are IFN sensitive and 3

(ISG15/USP18/CEB1) are linked in a novel IFN-regulatory pathway, suggesting a possible

rationale for treatment resistance. Using a number of independent classifier analyses, an 8-gene

subset accurately predicted treatment response for 30 of 31 patients. The classifier analyses were

applicable to patients with genotype 1 infection and were not correlated with viral load, disease

activity, or fibrosis. Conclusions: Hepatic gene expression profiling identified consistent

differences in patients who subsequently fail treatment with pegylated IFN-α plus ribavirin: up-

regulation of a specific set of IFN-responsive genes predicts nonresponse to exogenous therapy.

These data may be of use in predicting clinical responses to treatment.

44

Abbreviations used in this paper: IFN, interferon; KNN, nearest neighbor analysis; LDA,

linear discriminant analysis; NR, nonresponder; PCA, principal components analysis; PCR,

polymerase chain reaction; R, responder

Introduction

More than 170 million people worldwide are infected chronically with hepatitic C virus (HCV).1,

2 Currently there is no vaccine or small molecule therapy for this disease, which can lead to liver

failure and cancer. The most effective treatment is pegylated interferon (IFN)-α plus ribavirin,

which has morbid side effects, variable cure rates, and high costs.1

Although the interaction of the virus with hepatic microenvironments creates a cellular state that

is nonresponsive to treatment,3, 4, 5

the underlying molecular mechanisms are unknown and it is

not possible to predict treatment outcomes before initiating therapy. Viral and host factors both

play a role; for example, infection with HCV genotypes 1 or 4 is associated with at best a 60%

response rate, and increasing degrees of hepatic fibrosis can decrease response rates.1 Mutations

in viral (NS5A, NS5B) and host (MxA, OAS, PKR) proteins can enhance (NS5A, NS5B) or

partially inhibit (MxA) the response to IFN-based treatment.6-10

Increased hepatic MxA protein

expression is associated with poorer treatment responses.11

While these results are intriguing, the

heterogeneity of viral and host phenotypes makes it unlikely that any single factor will accurately

predict the cellular response to treatment.

The ultimate response to treatment can only be gauged after treatment with pegylated IFN-α plus

ribavirin has been initiated. Patients undergo at least a 12-week course of combination therapy

and then are assessed for an antiviral response. An early viral response (2-log decrease in

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45

baseline HCV RNA titers) suggests the eventual outcome, although only with 60%–90%

accuracy.1 However, this 3-month regimen is associated with maximum morbid side effects and

is expensive.1,12

We hypothesized that pretreatment nonresponder (NR) and responder (R) liver

tissue would show consistent differences in gene expression levels and that these differences

could be used to predict treatment outcomes.

Materials and Methods

Patients and Biopsies

Chronic HCV

Thirty-one patients with chronic HCV (23 genotype 1, 4 genotype 2, 3 genotype 3, and 1

genotype 6) were treated at University Health Network from October 2001 to May 2004. All

treatment-naive patients considering treatment with IFN/ribavirin underwent percutaneous liver

biopsy (via a 15-gauge needle) and had baseline viral loads determined. Treatment consisted of

pegylated IFN-α2a/2b 180 μg weekly by subcutaneous injection and oral ribavirin 800–1200 mg

daily (depending on genotype and weight) for 24 (genotype 2/3) or 48 (genotype 1/6) weeks.

Quantitative HCV RNA was determined at completion of therapy and 6 months after. Patients

were designated as NRs if HCV RNA was detectable at the end of therapy, as relapsers if HCV

RNA was undetectable at the end of treatment but was detectable at the 6-month follow-up, and

as having a sustained viral response if HCV RNA was undetectable at both the end of therapy

and the 6-month follow-up. Compliance was excellent (30 of 31 patients completed therapy). For

the purposes of this study, patients were designated as Rs if the initial post-treatment HCV RNA

titer was negative.




46

Normal liver tissue

Biopsies were performed on normal (HCV-negative) liver tissue as the first step of 20 right

hepatectomy operations performed on living transplant donors.

Ethics

All patients gave informed consent for the research protocol, which was approved by the hospital

research ethics board.

RNA Extraction and Amplification

A portion of each liver biopsy specimen (0.5–1.0 cm if percutaneous 15-g core) was immersed in

RNAlater (Qiagen, Mississauga, Ontario, Canada). Total RNA was extracted,13

and 2 μg of total

RNA from each biopsy specimen or from Universal Human Reference RNA (Stratagene, La

Jolla, CA) was amplified using the MessageAmp aRNA kit (Ambion, Austin, TX). Gene

expression profiles from amplified RNA were highly correlated to those developed from

nonamplified RNA (correlation coefficient ≥0.85, data not shown).

Complementary DNA Microarrays

Human single spot microarrays comprising 19,000 human clones were used (UHN Microarray

Center; http://www.microarrays.ca/support/glists.html). For each array, 5 μg of liver amplified

antisense RNA was compared with 5 μg of reference amplified antisense RNA. After reverse

transcription, liver complementary DNA was labeled with Cy5 and reference RNA with Cy3.13

Hybridization was performed overnight at 37°C (DIGEasy; Roche Diagnostics, GmbH,

Mannheim, Germany). Arrays were read with a GenePix 4000A laser scanner and quantified


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with GenePix Pro software (Axon Instruments, Union City, CA). Microarray data were

normalized using an R-based, intensity-dependent LOWESS scatter plot smoother

(http://142.150.56.35/ LiverArrayProject/home.html).14-16

Real-Time Polymerase Chain Reaction

Two-step real-time polymerase chain reaction (PCR) was performed after reverse transcription

of 5 μg of amplified antisense RNA with 5 μg pd(N)6-random hexamer primer (Amersham,

Oakville, Ontario, Canada). The resulting complementary DNA was used as a template for real-

time PCR quantification with the QuantiTect SYBR PCR Kit (Qiagen), and real-time PCR

(normalized to β-actin) was performed using the DNA Engine Opticon 2 cycler (MJ Research,

Reno, NV). For primers, see Table 2-1.

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Table 2-1. Real-Time PCR Primers for 18 differentially-expressed genes in the pretreatment

livers of Rs and NRs

Clone

ID Forward primer Reverse primer

PCR

product

length (base

pairs)

Gene name

229295 CAGACCCTGACAATCCACCT AGCTCATACTGCCCTCCAGA 164 Ubiquitin-specific protease 18

37942 GATTGCTGGAGGGAATCAAA TTGGATTTCCCTTTTTGTGC 160 Cyclin E binding protein 1

149319 CGCAGATCACCCAGAAGATT GCCCTTGTTATTCCTCACCA 185 IFN-α–inducible protein 1

136508 TCAGCGAGGCCAGTAATCTT GCAGGACATTCCAAGATGT 154 2′,5′-oligo adenylate synthetase 2

324912 CTCGCTGATGAGCTGGTCT ATACTTGTGGGTGGCGTAGC 148 IFN-α–inducible protein (clone IFI-6-16)

324284 GTCAAACCCAAGCCACAAGT GGGCGAATGTTCACAAAGTT 110 2′,5′-oligoadenylate synthetase 3, 100 kilodaltons

5474956 GCTGTAGCCGTCTCTGCTG AAAAAGGCCAAATCCCATGT 135 Ribosomal protein, large P2

325364 GCAGCCAAGTTTTACCGAAG GCCCTATCTGGTGATGCAGT 109 IFN-induced protein with tetratricopeptide repeats 1

120600 CTTTTGCTGGGAAGCTCTTG CAGCTGCTGCTTTCTCCTCT 131 Viperin

176650 CCGTGTGCAGCCTATCAAG TTTACATTGCGGATGATGGA 129 RPS28

5745506 CTGCAGAGAGCTTTCCATCC GTCTCTGGCTCATCGTCACA 134 Phosphoinositide-3-kinase adaptor protein 1

325130 GTGCATTGCAGAAGGTCAGA CTGGTGATAGGCCATCAGGT 140 Myxovirus (influenza virus) resistance 1

52905 CCAACCATTTTGAGGGTCAC ACCCTTCCTCCAGCATTCTT 130 Dual specificity








49

Clone

ID Forward primer Reverse primer

PCR

product

length (base

pairs)

Gene name

phosphatase 1

127270 AGCCCCCTGTCTTGGATACT CGAGAAGGTTGAGGTGGAGA 133 Activating transcription factor 5

487534 GGTGCCATGGATGTAGCTTT AGAGAGGCATCCTCCAGACA 124 Leucine aminopeptidase 3

207669 GCAGGAAGACAGTGGAGAGC GAGCCAGCACTTCTGGGTAG 125 D11lgp1e-like

3930678 AGCGGAAGGAGGAGAAAAAG GTACTCTTGGGCAGGTGAGC 121 Eukaryotic translation elongation factor 1 γ

231624 GTTCATCTGATGGGCTTCGT TTTGTTGTGGTGGTTCTCCA 132 Syntaxin binding protein 5 (tomosyn)

Statistics, Clustering, and Classifier Analyses

Comparisons between continuous variables were performed using the 2-sample Welch t statistic

with the multtest package, which includes an estimation of adjusted P values by permutation.17

Unsupervised hierarchical clustering and unsupervised principal components analyses (PCA)

were performed using the R mva package.18,19

Nearest neighbor classifier analyses (KNN) were

performed using the R class package, and linear discriminant analyses (LDA) were performed

with the R MASS package.20,21

Details can be found at

http://142.150.56.35/LiverArrayProject/home.html.









50

Results

A Gene Expression Profile That Discriminates Rs and NRs

The patients in this study were well matched for most clinical variables with the exception of

viral genotype and sex (Table 2-2). There were no significant differences between R and NR

patients when compared for age, baseline viral load, disease activity, hepatic fibrosis, compliance

to therapy, or dose reduction. Patients with genotype 1 infection had the highest failure rate with

therapy and accounted for all NR patients in our cohort.


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Table 2-2. Patient Characteristics: All 31 Patients

Variable NR R P

No. 15 16

Age (y) 46.4 ± 14 48.3 ± 10 .6896

Sex (no. male) 7/15 13/16 .0443a

Genotype 1 infection 15/15 8/16 .0015a

Viral load (IU/mL) 2.4 × 106 ± 3.7 × 106 3.8 × 106 ± 4.3 × 106 .3529

Activity 1.63 ± 0.44 1.81 ± 0.51 .3049

Fibrosis 2.50 ± 0.84 2.65 ± 0.94 .6305

Completed course of treatment 14/15 16/16 .72

Pegylated IFN-α plus ribavirin dose >80% 14/15 12/16 .69

Alcohol (10 drinks/week) 2/12 2/13 .66

Smoking (1 pack/day) 5/9 4/8 .74

Race (no. black) 3/15 0/16 .083

NOTE. All patient characteristics were recorded in a prospectively maintained database. In general, data are

presented as mean ± SD. Where data are presented in fractions, the denominator represents the number of patients

for whom full data were available. Statistics are either Welch t test or χ2 analysis. The number of patients who

received at least 80% of the dose of pegylated IFN-α plus ribavirin for at least 80% of the time was recorded over

the entire course of therapy.

a P < .05

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To define which genes discriminate between HCV infection of Rs and NRs, we compared gene

expression levels from 15 NR, 16 R, and 20 normal liver biopsy specimens. We determined that

the levels of 18 genes differed between R and NR groups with P < .005 (Table 2-3) and verified

these differences using real-time PCR (Figure 2-1). Within these 18 genes, most of the difference

between NR and sustained virologic response samples was a relative up-regulation in NR tissue;

R gene expression profiles actually cocluster with normal liver (Figure 2-2). Hierarchical cluster

analysis clearly segregated all NR samples in one family, with all but 2 R samples and all normal

liver samples segregated in another cluster (Figure 2-2). Thus, there is a consistent difference in

the NR response to HCV reflected in the expression of 18 genes.


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53

Table 2-3. Eighteen Genes That Differ Between NR and R Hepatic Gene Expression Profiles

Clone

ID Name Symbol NR/R

P

(NR

vs R)

NR/normal P (NR vs

normal) R/normal

P (R vs

normal)

149319a IFN-α–inducible protein (clone IFI-

15K)c

G1P2/ISG15/IFI15 4.37 .0001 9.69 .0001 2.22 .0001

136508a 2′, 5′-oligoadenylate

synthetase 2c OAS2 3.80 .0001 6.58 .0001 1.73 .0009

324912a

IFN-α–inducible

protein (clone IFI-6-16)c

G1P3/IFI616 2.83 .0001 4.72 .0001 1.67 .0002

324284a 2′, 5′-oligoadenylate synthetase 3c

OAS3 2.54 .0001 3.42 .0001 1.35 .005

5474956a Ribosomal protein, large P2

RPLP2 2.53 .0001 3.70 .0001 1.46 .0002

37942a Cyclin E binding protein 1c

CEB1 2.15 .0001 2.55 .0001 1.19 .0777

325364a IFN-induced protein with tetratricopeptide repeatsc

IFIT1 2.14 .0001 2.83 .0001 1.32 .0127

120600a Viperinc VIPERIN/cig5 1.82 .0002 1.78 .0001 0.98 .8031

176650a 40S ribosomal protein S28

RPS28 1.75 .0004 2.38 .0001 1.35 .0002

5745506a Phosphoinositide-3-kinase adaptor protein 1

PI3KAP1 1.60 .005 1.66 .0022 1.04 .8283

325130a

Myxovirus (influenza virus) resistance 1, IFN-inducible protein p78c

MX1 1.58 .0013 1.98 .0001 1.25 .0394

52905a Dual specificity phosphatase 1

DUSP1 1.56 .0003 0.59 .002 0.38 .0001

127270a Activating transcription factor 5

ATF5 1.56 .0046 0.96 .6984 0.62 .0024

487534a Leucine aminopeptidase 3

LAP3 1.56 .0003 2.10 .0001 1.35 .0067

229295a Ubiquitin-specific protease 18

USP18/UBP43 1.52 .0001 1.72 .0001 1.13 .0791

207669a D11lgp1e-like LGP1 1.51 .0014 1.38 .0094 0.92 .1351

























54

Clone

ID Name Symbol NR/R

P

(NR

vs R)

NR/normal P (NR vs

normal) R/normal

P (R vs

normal)

3930678b Eukaryotic translation elongation factor 1 γ

ETEF1 0.65 .0032 0.75 .0009 1.15 .7341

231624b Syntaxin binding protein 5 (tomosyn)

STXBP5 0.65 .0034 0.96 .7156 1.47 .0126

NOTE. Gene expression ratios were compared among NR, R, and normal liver gene expression values. Statistics are

calculated using the Welch t test.

aUp-regulated in NR.

bDown-regulated in NR.

cIFN-sensitive gene.



55

Figure 2-1. Real-time PCR verification of genes predicted to be altered by microarray.

Figure 2-1. Real-time PCR verification of genes predicted to be altered by microarray. Real-time PCR verification

was performed as described in Materials and Methods. In all cases, 4 genotype 1 R samples were compared with 4

genotype 1 NR samples and 3 normal liver samples (white, normal; gray, R; black, NR). Values along the y-axis

represent the ratio, in arbitrary units, of a given gene versus β-actin. Data are expressed as mean ± SEM. *P < .05

NR versus R; Welch t test. OAS-3, P = .054; CEB-1, P = .105; RPS28, P = .053; STXBP5, P = .12.

56

Figure 2-2. Hierarchical cluster analysis using the 18 genes present in all 31 samples

Figure 2-2. Hierarchical cluster analysis using the 18 genes present in all 31 samples. Unsupervised hierarchical

cluster analysis was performed as described in Materials and Methods, restricting the analysis to the 18 genes in

Table 2-3. Red denotes an increase and green a decrease when compared with the reference RNA pool. An asterisk

denotes the patients who experienced a relapse following treatment with pegylated IFN-α plus ribavirin. Note that

normal liver tissue coclusters with patients who respond to treatment, while all NR samples form part of a discrete

cluster.

57

A Gene Subset Will Accurately Differentiate NRs and Rs

Unsupervised cluster analyses do not assign predictive end points (in this case, response to

treatment). To determine whether the genes that differed between R and NR tissue could be used

to predict treatment response, we used 2 supervised classifier analyses (KNN and LDA) and

corroborated these results with a further unsupervised cluster analysis (PCA). Because different

gene combinations will have different predictive abilities, we randomly drew 50,000

combinations of 6, 8, 10, 12, and 14 genes from Table 2-3 and assessed their individual ability to

correctly classify the 31 NR and R samples. We determined a subset of 8 genes with the most

consistent ability to correctly classify NR and R samples, comprising GIP2/ IFI15/ISG15, ATF5,

IFIT1, MX1, USP18/UBP43, DUSP1, CEB1, and RPS28.

Using this predictive gene subset, unsupervised hierarchical cluster analysis identified 2 clusters:

one comprised of all NR samples but one, and the other of all R samples but one (Figure 2-3A).

Both KNN and LDA accurately identified 30 of 31 samples, while PCA clearly separated R and

NR samples into 2 distinct groups (Figure 2-3B). For comparison, if all 18 genes are used, the

KNN prediction rate decreases to 28 of 31.




58

Figure 2-3. Cluster and classifier analysis using the 8-gene predictor set: all patients.

Figure 2-3. Cluster and classifier analysis using the 8-gene predictor set: all patients. (A) Hierarchical cluster

analysis of all samples, restricting the analysis to the 8 genes in the predictor set. (B) KNN, LDA, and PCA of all

samples using the 8-gene predictor set. Unsupervised (hierarchical cluster, PCA) and supervised (KNN, LDA)

analyses were performed as described in Materials and Methods. As in Figure 2-2, an asterisk denotes treatment

relapsers.

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59

Because patients with genotype 1 infection are the least likely to respond to treatment, and

because classifier analyses are influenced by the numbers and characteristics of the samples in a

teaching set, we examined whether the predictive gene subset was valid within the 23 patients

with genotype 1 infection in our cohort. As shown in Table 2-4, there were no significant clinical

differences in these patients. The predictive gene subset correctly classified 21 of 23 samples

using KNN and LDA, while PCA and unsupervised hierarchical clustering again clearly created

2 distinct clusters (Figure 2-4). Together, our results argue that a gene subset can predict NR and

R status independent of genotype.



60

Table 2-4. Patient Characteristics: Patients With Genotype 1 Infection Only

Variable NR R P

No. 15 8

Age (y) 50.2 ± 5.1 43.9 ± 9.0 .1032

Sex (no. male) 7/15 6/8 .1917

Viral load (IU/mL) 2.40 × 106 ± 3.7 × 106 4.87 × 106 ± 5.1 × 106 .2597

Activity 1.63 ± 0.44 1.75 ± 0.46 .5681

Fibrosis 2.50 ± 0.84 2.56 ± 0.98 .881

Completed course of treatment 13/14 7/7 .85

Pegylated IFN-α plus ribavirin dose >80% 14/15 7/8 .72

Alcohol (10 drinks/wk) 2/12 2/5 .41

Smoking (1 pack/day) 5/9 ¾ .76

NOTE. Patient characteristics restricted to those patients infected with genotype 1 HCV. Again, data are presented

as mean ±SD. Where data are presented in fractions, the denominator represents the number of patients for whom

full data were available. Statistics are either Welch t test or x2 analysis.

61

Figure 2-4. Cluster and classifier analysis using the 8-gene classifier set: patients with genotype 1 infection only

Figure 2-4. Cluster and classifier analysis using the 8-gene classifier set: patients with genotype 1 infection only. A

hierarchical cluster analysis of the 23 genotype 1 samples, restricting the analysis to the 8 genes in the predictor set.

(B) KNN, LDA, and PCA of genotype 1 samples using the 8-gene predictor set. Unsupervised (hierarchical cluster,

PCA) and supervised (KNN, LDA) analyses were performed as described in Materials and Methods. As in Figure 2-

2, an asterisk denotes treatment relapsers.



62

Discussion

Our study compared hepatic gene expression profiles from liver biopsy specimens taken from 31

patients before treatment with pegylated IFN-α plus ribavirin. We identified 18 genes, confirmed

by real-time PCR, with expression levels that differed consistently between NR and R liver

tissue and were not correlated to any obvious clinical parameter. The raw data set can be

accessed at http://142.150.56.35/ LiverArrayProject/home.html. Levels for these 18 genes in R

liver were closer to uninfected tissue than to NR liver, with a general up-regulation of gene

expression in NR liver. Interestingly, many of these genes are IFN responsive, suggesting that

the NR patients have adopted a different, yet characteristic, equilibrium in their host-virus

immune response.

Although this study examined a relatively small set of patients (31), 3 arguments suggest that the

results are broadly applicable. First, although the discriminatory genes were identified based

solely on mathematical grounds, several have been previously linked either to HCV infection or

to the response to viral infection. For example, polymorphisms of OAS have been weakly linked

to self-limited HCV infection and polymorphisms of Mx1 have been weakly linked to response

status.9 Hepatic messenger RNA levels for OAS, Mx1, and GIP2 are increased in chronic HCV,

but none alone have been linked to treatment outcome.11 ,22

Many of the others are IFN-sensitive

genes with antiviral activity and are consistent with an alteration in IFN responsiveness being

linked to treatment nonresponse. The genes that are not directly IFN responsive may play roles in

cellular pathways important for IFN responses (PI3AP1, DUSP1)23,24

and are involved in

inflammatory cell activation and maturation.25,26

Second, the predictive subset of 8 genes

performed well across all 4 statistical analyses (hierarchical clustering, KNN, LDA, and PCA).

Third, the composition of the classifier set was unrelated to confounding clinical factors, such as










63

viral load, degree of fibrosis, and age. In multivariate analyses, USP18 expression was

significantly affected by degree of fibrosis (data not shown), but none of the other 17 genes were

linked to any of the clinical factors.

Two genes in the classifier gene set, ISG15/IFI15 and USP18/UBP43, are noteworthy for

belonging to a novel IFN-regulatory pathway. In our study, NR and R patients were

distinguished by up-regulated expression of ISG15 and USP18 in their pretreatment liver biopsy

tissue. ISG15 is a ubiquitin-like protein that is believed to be important to innate immune

functions.27

The USP18/UBP43 protease specifically removes ISG15 from ISG15-modified

proteins28

; loss of USP18 in mice leads to IFN hypersensitivity.29

The USP18-ISG15 pathway is

important in innate immunity against viral infection; a recent, elegant study showed that USP18

knockout mice are resistant and wild-type mice are susceptible to fatal intracerebral infection by

lymphocytic choriomeningitis virus or vesicular stomatitis virus, concurrent with decreased viral

replication and increased protein ISGylation in the knockout mice.30

Although the investigators

suggest that the pathway may be relevant to human disease, ours is the first demonstration of the

potential relevance of the USP18-ISG15 pathway in human viral infection. In our study, USP18

up-regulation was one of the factors predicting a lack of response to treatment with IFN,

consistent with a role for USP18 in modifying the antiviral IFN response.

In conclusion, our study shows that NR and R patients differ fundamentally in their innate IFN

response to HCV infection. These differences suggest novel aspects of HCV pathogenesis and

form the basis for a predictive subset of genes that can predict treatment responses before

initiation of pegylated IFN-α plus ribavirin therapy.22






64

Ackowledgement:

The authors thank the physicians and surgeons of the Toronto Multi-Organ Toronto Transplant Program for their

support and interest, particularly Drs David Grant, Mark Cattral, Paul Greig, and Gary Levy, and thank Drs

Elizabeth Edwards and Kaiguo Mo for assistance with the real-time polymerase chain reaction studies. A.M.E. is the

Banbury Chair of Medical Research. The authors also thank the CIHR National Research Training Program–HCV

for its interest in this study.

65

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29. Malakhova OA, Yan M, Malakhov MP, Yuan Y, Ritchie KJ, Kim KI, Peterson LF, Shuai

K, Zhang DE. Protein ISGylation modulates the JAK-STAT signaling pathway. Genes

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68

Chapter 3: Confirmation of the HCV response signature-prospective validation and cell type-

specific expression of ISG proteins identified by immunohistochemical staining

The HCV response signature was identified in a retrospective study. In order to know whether

this signature could be used to predict treatment response prospectively, 78 patients chronically

infected with HCV were recruited. Liver biopsies were obtained prior to the initiation of

treatment and the expression levels of host genes were determined on a cDNA microarray chip

containing 19,000 genes/ESTs.

Protein expression of 2 ISGs (ISG15 and MxA) in paraffin-embedded liver biopsy tissue was

examined by immunohistochemistry(IHC) study and the protein expression patterns were

correlated with response status.

This piece of work entitled ―Cell-type specific gene expression signature in liver underlies

response to interferon therapy in chronic hepatitis C infection” was published in

Gastroenterology 2010;138(3):1123-1133.e3. Epub 2009 Nov 6.

My role in this publication: study concept and design, acquisition of data, analysis and

interpretation of data, drafting and revising of the manuscript.

Reprinted from Gastroenterology, Vol 138, Chen, et al. Cell-type specific gene expression

signature in liver underlies response to interferon therapy in chronic hepatitis C infection, Page

1123-1133, copyright (2010), with permission from Elsevier.

69

Cell-type specific gene expression signature in liver underlies response to interferon

therapy in chronic hepatitis C infection

Limin Chen1,2

, Ivan Borozan1, Jing Sun

1, Maha Guindi

3, Sandra Fischer

3, Jordan Feld

4, Nitasha

Anand4 , Jenny Heathcote

4, Aled M. Edwards

1,2,5, Ian D. McGilvray

4,6*

From the Banting and Best Department of Medical Research1, Department of Molecular

Genetics2, Departments of Pathology

3, Medicine

4, Medical Biophysics

5, and Surgery

6, University

of Toronto, Toronto, Ontario

None of the authors have financial disclosures.

No conflict of interests exist.

Keywords: HCV response signature, gene expression profiling, prospective validation,

multivariate analysis, immunohistochemistry

Running title: HCV treatment response signature validation

Grant support: This work was funded by grants from the Canadian Institute of Health Research

(No. 62488 to I.D.M). L.C. and I.B are supported by the National Canadian Research Training

Program in Hepatitis C (NCRTP-HepC). L.C is also supported by Canada Graduate Scholarship

(CGS) from the Canadian Institute of Health Research.

70

*Corresponding author:

Ian D. McGilvray MD, PhD

Assistant Professor of Surgery

11C1250 Toronto General Hospital

585 University Avenue

Toronto, Ontario M5G 2N2 Phone: 416 340 4190 Fax: 416 340 5242

e-mail: ian.mcgilvray@uhn on.ca

Abbreviations :

CHC: chronic hepatitis C

HCV: hepatitis C virus

IFN: interferon

mAb: monoclonal antibody

NR: non-responder

pAb: polyclonal antibody

pegIFN/rib: pegylated interferon/ribavirin

R: responder

Rib: ribavirin

SVR: sustained virological response

71

ABSTRACT:

Background & Aims: Chronic hepatitis C virus (CHC) infection is treated with

interferon/ribavirin, but only a subset of patients respond. Treatment nonresponders have marked

pre-treatment upregulation of a subset of interferon stimulated genes (ISGs) in their livers,

including ISG15. We here study how the nonresponder gene expression phenotype is influenced

by clinical factors, and uncover the cellular basis of the phenotype through ISG15 protein

expression.

Methods: 78 CHC patients undergoing treatment were classified by clinical (gender, viral

genotype, viral load, treatment outcome) and histological (inflammation, fibrosis) factors and

subjected to gene expression profiling on their pre-treatment liver biopsies. An ANOVA model

was used to study the influence of individual factors on gene expression. ISG15

immunohistochemistry was performed on a subset of 31 liver biopsies.

Results: 123 genes were differentially expressed in the 78 CHC livers when compared to 20

normals (p <0.001, fold change ≥ 1.5 fold). Of genes influenced by a single factor, genotype (1

vs 2/3) influenced more genes (17) than any other variable; when treatment outcome was

included in the analysis, this became the predominant influence (24 genes), and the effect of

genotype was diminished. Treatment response was linked to cell-specific activation patterns:

ISG15 protein upregulation was more pronounced in hepatocytes in treatment nonresponders, but

in Kuppfer cells in responders.

Conclusions: Genotype is a surrogate marker for the nonresponder phenotype. This phenotype

manifests as differential gene expression and is driven by activation of different cell types:

hepatocytes in treatment nonresponders, and macrophages in treatment responders.

72

INTRODUCTION

Almost 3% of the world‘s population is infected with hepatitis C virus (HCV). 1,2

It is the most

common newly diagnosed cause of liver disease and the most common reason for a liver

transplant.3 Treatment of chronic hepatitis C (CHC) is difficult: the current standard of care is

pegylated-interferon IFNα (PegIFN) combined with ribavirin.4 This regimen has only a 50%

response rate overall, with high costs and high morbidity.1

Why patients respond to IFN-based treatment differently likely reflects differences in the

viral/host response. On the viral side, genotype is the single most important factor predicting

treatment response.5 Patients infected with HCV genotypes 2 and 3 respond considerably better

than those infected with genotypes 1 and 4,1 with response rates of approximately 80%,

compared to 45% for genotype 1.6-8

Viral load at the initiation of therapy is less predictive,

though SVR is achieved more often in patients with low titers (<800 000 IU/ml).6-8

Host factors

are also involved. Hepatic fibrosis is important, with fewer responders in patients with advanced

hepatic fibrosis.9 Race may also play a role: African-American patients tend to have lower

response rates to therapy than Caucasian patients, while Asian patients have higher response

rates. 10-13

There are also genetic bases for treatment response: polymorphisms in inflammatory

genes, such as IFNγ and IL-10, have been shown to be associated with rates of response. 14,15

We have shown that differences in hepatic gene expression levels determined from liver biopsies

obtained prior to treatment initiation are associated with treatment outcome.16

Genes strongly

73

correlated with response status are enriched in interferon stimulated genes (ISGs); their levels of

expression are higher in nonresponders (the ―high ISG‖ group) than in responders (the ―low

ISG‖ group).16

Among the ISGs correlated with response, ISG15 (a ubiquitin-like protein) is

consistently upregulated in the pre-treatment liver tissue of patients who then do not respond to

PegIFN/Rib.17-19

Among all the factors associated with treatment response viral genotype and the host gene-

expression profiles may correlate best with response.17-19

However, it has not been established

whether the individual host and viral factors are independent markers of treatment response, or if

some are simply surrogates for others. In this study we extend our initial observations16

by

considering a larger CHC population (78 patients). We describe results that strongly argue that

cell-specific gene expression patterns define two states of CHC infection that influence treatment

response.

74

MATERIALS AND METHODS

Patients and liver biopsies:

Chronic HCV: 78 patients with CHC (56 genotype 1, 22 genotype non-1) were treated at

University Health Network from October 2001 through May 2004. All patients underwent

pretreatment percutaneous liver biopsy and had baseline viral loads determined. Treatment was

either PegIFN2a 180 g/kg s/c weekly, and oral ribavirin 800-1200mg daily

for 24 (genotype 2/3) or 48 (genotype 1, 4 and 6) weeks. Quantitative HCV-RNA was

determined at completion of therapy and 6 months after (lowest limit of detection, LLD 50

IU/ml, Roche). A patient was designated a nonresponder (NR) if HCV-RNA was detectable at

the end of therapy, as a relapser if HCV-RNA was undetectable at the end of treatment but was

detectable at the 6 month follow-up, and as having a sustained viral response (SVR) if both end-

of-treatment and 6 month follow-up HCV-RNA were undetectable. For the purposes of this

study, responders (R) were considered to be all patients who had no detectable HCV RNA at the

end of treatment.

Normal liver tissue: Normal liver tissue was obtained as the first step of a living donor right

hepatectomy in 20 HCV-, HBV-negative patients.

Ethics: All patients gave informed consent for the research protocol, which was approved by the

hospital Research Ethics Board.

RNA extraction, amplification, and microarray analysis:

75

A portion of each liver biopsy (0.5-1.0cm if percutaneous core) was immersed in RNAlater

(Qiagen), RNA was extracted and amplified, and gene expression levels were determined using

human single spot 19000 clone cDNA microarrays as in our previous studies (UHN Microarray

Center).16,20,21

Microarray data was normalized using an R-based, intensity-dependent lowess

scatter plot smoother as before.16,20,22

Statistical analysis:

Statistical significance of differences between measured gene expressions levels was evaluated

using a two-sample Welch t-statistic implemented in the R package multtest, which includes an

estimation of adjusted p-values by permutation.23

Comparisons of clinical, demographic and

histological categorical variables were done using either a Fisher's exact test or a Chi-squared

test.23

Unsupervised hierarchical clustering was performed using the R package stats.23

Where

appropriate, Student‘s t test was used to compare 2 categorical values and one-way ANOVA was

used to compare more than 2 categorical values. For western blot studies the presented work is

representative of at least three independent experiments.

ANOVA model for microarray experiments:

Our ANOVA model contains four clinical factors (gender, viral genotype, viral load, treatment

response) and two histological factors (disease activity, fibrosis). The full model was fitted on a

gene-by-gene basis to log2-transformed expression ratios of CHC and normal liver. Our model

assumes all effects to be fixed. The main objective of the analysis was to identify genes

76

with significant changes between the levels of each factor after adjusting for all other effects

specified in the model. The significance of each computed effect is thus relative only to other

factors present in the model. Because our data are unbalanced we used a type III sum of squares

approach where the sums of squares is based on comparing the full model to models with each

factor removed one at a time. In this way the calculated sum of squares for unbalanced datasets is

independent of the order of factors used. The significance of each effect is then computed with

the F-test. For the sake of clarity we identified genes that most clearly were influenced by a

single factor: genes for which there were significant interactions between factors are not

presented.

Immunohistochemical studies:

All percutaneous liver biopsies were fixed in 10% neutral buffered formalin, paraffin-embedded,

sectioned at 4μm and stained with hematoxylin/eosin. MxA immunostaining - antigen was heat-

retrieved with Tris-EDTA pH9.0, stained with anti-MxA mAb (M143, Dr. Otto Haller,

University of Freiburg, Germany) at 1:300 for 1hr and finished with MACH4 UniversalAP

Polymer Kit (Biocare Medical, Concord, CA). ISG15 immunostaining - antigen was heat-

retrieved with citrate buffer at pH6.0, stained with rabbit anti-human ISG15 pAb (developed in

our laboratory) at 1:300 for 1 hr and finished as above. CD68 immunostaining- antigen was

retrieved by pepsin digestin and incubated with anti-CD68 mAb (PG-M1, Dako ) at 1:100 for 1hr,

and finished with streptavidin-biotin-HRP (ID Labs Inc). Smooth muscle actin (SMA)

immunostaining: anti-SMA mAb (Sigma, clone 1A4) was used at 1:3000 for 1 hr and finished

with streptavidin-biotin-HRP as above. Endogenous peroxidase and biotin activities were

77

blocked with 3% aqueous H2O2 and Lab Vision avidin-biotin blocking kit. ISG15 quenching test:

First, 10ng of purified human ISG15 in each of 6 lanes was separated on SDS-PAGE gel and

transferred onto nitrocellulose membrane. This was cut into 6 lanes and incubated 2hrs with anti-

ISG15 pAb (1:300) and increasing concentrations of purified ISG15 (0, 2, 20, 200, 2000,

4000ng/ml); the film was developed with OptiBlaze WESTfemtoLUCENT (G-Biosciences, MO,

USA). Second, MxA or ISG15 antibody was neutralized with 9 volumes of recombinant ISG15

(2μg/ml) overnight (4oC) and then used for IHC at 1:300.

Cell staining for ISG15 or MxA was assessed throughout the entire length of each core of each

biopsy (≥30 high power fields per biopsy). An immunohistochemical score was assigned based

on the proportion of immunoreactive cells in a given cell type. Staining in individual cell types

(hepatocyte, macrophage, lymphocyte, bile duct epithelium) was scored from 0 (no staining in

any cell), 1 (occasional cell), 2 (multiple cells), to 3 (virtually all cells). Various semiquantitative

scoring systems for evaluation of immunohistochemical staining have been developed – ours was

chosen for its ease and reproducibility.24-26

The evaluation was done in a blinded manner by two

independent liver pathologists; the evaluation was repeated, with the samples re-arranged, again

in a blinded manner, several days after the first evaluation.

78

Results:

Patient demographics :

Demographic and clinical variable data on the 78 CHC patients are summarized in Table 3-1.

The majority (72%) were infected with HCV genotype 1. Patients were denoted ―low fibrosis‖ if

their Ishak scores were 0-2, and ―high fibrosis‖ if their scores were 3-4.27

Similarly, patients

were classified ―low activity‖ if their METAVIR A scores were 0-2 and ―high activity‖ for

scores of 3-4. For this study, high viral load was defined as >8x106 IU/ml. Comparing genotype

1 and non-1 patients there were no statistical differences in clinical (age, sex, viral load) or

histological variables (disease activity, degree of fibrosis). In total, there were 23 non-responders

and 55 responders (42 SVR, 13 relapsers).

79

Table 3-1. Patient demographics

Genotype 1 (56) All other

Genotypes (22)

P Value

Age

Mean 50.87 +/- 9.87 51.64 +/- 10.22 0.77

Fibrosis Low 33 (59%) 12 (55%) 1

High 23 (41%) 10 (45%) 0.82

Activity Low 33 (59%) 12 (55%) 1

High 23 (41%) 10 (45%) 0.82

Gender M 38 (68%) 13 (59%) 0.84

F 18 (32%) 9 (41%) 0.63

Viral Load Low < 8x106 IU 32 (57%) 17 (77%) 0.55

High > 8x106 IU 24 (43%) 5 (23%) 0.32

Response NR 21 (38%) 2 (9%) 0.09

SVR 25 (45%) 17 (77%) 0.22

Relapsers 10 (17%) 3 (14%) 1

80

Predictive value of an 18-gene “responder” signature:

We first sought to confirm the phenotypes described in our earlier study. After gene expression

profiling on the 78 liver biopsies (31 from the original study,16

and 47 additional ones) we asked

whether the eighteen-gene signature described in our earlier study segregated samples by

treatment response. We performed a hierarchical cluster analysis, independent of patient

identifiers. As shown in Figure 3-1, two clusters resulted: one that was highly enriched for

treatment responders, the other for nonresponders.

We next asked whether expression levels for the 18 genes could classify patient samples as

―responder‖ or ―nonresponder.‖ We used four different families of classifiers, all validated in

previous microarray classification studies:28

k-nearest neighbor (KNN), diagonal quadratic

(DQDA) and linear discriminant analysis (DLADA), and classification and regression trees

(CART). Misclassification rates for each classifier were estimated over 100 runs using a 2:1

sampling scheme based on random divisions of learning and test sets (2/3 and 1/3 of the data,

respectively). All four classifiers had very similar results (Table 3-2). In all cases there was a

better prediction rate for R (PPV 0.96) than for NR (NPV 0.58). Overall, these results describe a

predisposition: patients with the ―low ISG‖ responder phenotype are much more likely to

respond to treatment than those with the ―high ISG‖ nonresponder phenotype .

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Table 3-2: Response signature validation based on gene expression data from cDNA

microarray

Methods Sensitivity specificity PPV NPV

KNN 0.78 +/- 0.08 0.81 +/-0.14 0.93 +/- 0.05 0.56+/- 0.09

DQDA 0.75+/- 0.10 0.91+/-0.10 0.97+/- 0.04 0.55+/-0.08

DLDA 0.73+/-0.08 0.92+/-0.09 0.97+/-0.04 0.53+/-0.08

CART 0.84+/-0.08 0.87+/-0.16 0.96+/-0.05 0.66+/-0.13

PPV: positive prediction value; NPV: negative prediction value; KNN: K-nearest neighbour; DQDA: Diagonal

Quadratic Discriminant Analysis; DLDA: Diagonal Linear Discriminant Analysis; CART: Classification and

Regression Trees

82

Figure 3-1 Hierachial clustering analysis of 78 HCV chronically infected liver samples

based on expression levels of 18 previously-defined signature genes

Figure 3-1. Hierachial clustering analysis of 78 CHC liver samples based on expression levels of 18 previously

defined signature genes. Hierarchial clustering analysis was performed based on the expression levels of 18

previously defined genes.16 The analysis broadly separates patients into 2 groups: responders (R) and nonresponders

(NR).

83

Multivariate analysis of microarray expression data:

The above results suggest that there is a persistent difference in the viral/host response, manifest

at the level of gene expression, influencing the response to treatment. We next asked whether

this difference was influenced by or was independent from the clinical factors that are used to

predict treatment responses (genotype, viral load, fibrosis). We performed a multivariate

ANOVA analysis on the genes identified as being consistently altered by CHC infection when

compared to uninfected, normal liver tissue. Taking a p value of 0.001 and a fold change (vs

normal liver tissue) of ≥1.5, a total of 123 genes was altered in CHC liver tissue. The full gene

list can be accessed from our lab server at http://142.150.56.35/~LiverArrayProject2/home.html.

(user name: lab; Password: samatalw41)

We first considered the influence of gender, viral load, genotype, disease activity, and fibrosis on

gene expression. Restricting the analysis to these five factors we could ascribe differences in

expression of 33 genes to single factors. Differences in viral genotype (genotype 1 vs genotype

2/3) were correlated with altered gene expression levels of the highest number of genes (17)

(Figure 3-2A). The genes most influenced by genotype 1 infection included DUSP1, OAS3,

LAP3, and RPS28, all genes that we previously identified as predictors of treatment response

(Table 3-3A).16

Real-time PCR confirmed the accuracy of these results (data not shown).

http://142.150.56.35/~LiverArrayProject2/home.html

84

Figure 3-2. Multivariate ANOVA analysis of genes altered by chronic HCV infection

A B

Figure 3-2. Multivariate ANOVA analysis of genes altered by chronic HCV infection. (A) Response status excluded:

The bar graph shows the number of genes significantly associated with each factor (genotype/ disease [D], gender

[G], viral load [V], fibrosis [F], and inflammation [I]). Genotype was associated with the most genes (17). The box

plots below show the distribution of fold differences in gene expression between the levels of each factor for

significant genes associated with each factor. (B) Response status included: In this analysis, response status (R) was

considered in addition to the 5 previous factors. Response status was associated with the most genes (24).

85

Table 3-3A: Genes affected by viral genotype (―response to treatment‖ excluded)

cloneID LOCUS Symb geneName Hepc/Nor p.value p.genotype

5725681 81671 VMP1 vacuole membrane protein 1 0.58 1.00E-04 0.01521314

5221374 51561 IL23A IL23A interleukin 23, alpha subunit p19 1.57 1.00E-04 0.020740392

26063 432395 C4B complement component 4B, telomeric 0.63 1.00E-04 0.039356361

108690 EST EST EST 1.67 1.00E-04 0.020127457

428195 51614 SDBCAG84 serologically defined breast cancer antigen 84 1.48 1.00E-04 0.003643768

52905 1843 DUSP1 dual specificity phosphatase 1 0.52 1.00E-04 0.015530487

471667 6772 STAT1 signal transducer and activator of transcription 1, 91kDa 1.77 1.00E-04 0.027999208

754047 23097 CDC2L6 cell division cycle 2-like 6 (CDK8-like) 1.73 1.00E-04 0.038349041

487534 51056 LAP3 leucine aminopeptidase 3 1.45 1.00E-04 0.000593948

24277 9246 UBE2L6 ubiquitin-conjugating enzyme E2L 6 1.54 1.00E-04 0.000806251

176650 441618 RPS28 40S ribosomal protein S28 1.85 1.00E-04 0.004459306

324284 4940 OAS3 2'-5'-oligoadenylate synthetase 3, 100kDa 1.96 1.00E-04 0.002096342

502921 83666 PARP9 poly (ADP-ribose) polymerase family, member 9 1.87 1.00E-04 0.007749409

485859 64108 IFRG28 28kD interferon responsive protein 1.52 1.00E-04 2.08E-05

491243 3627 CXCL10 chemokine (C-X-C motif) ligand 10 2.73 1.00E-04 0.010216357

5474956 6181 RPLP2 ribosomal protein, large P2 2.74 1.00E-04 0.005944298

485870 55601 FLJ20035 hypothetical protein FLJ20035 2.37 1.00E-04 0.00042658

Hepc/Nor, ratio of the gene expression level in CHC-infected liver to normal liver; P value, P value of Hepc/Nor;

P genotype, P value for the viral factor genotype.

NOTE. Response to treatment excluded.

86

In the next multivariate ANOVA model analysis, we included ―response to treatment‖ as a

variable. In this analysis we could link 54 genes to individual variables. As presented in Figure

3-2B, ―response to treatment‖ became the single most predominant influencing variable (24

genes). At the same time, the effect of genotype was markedly diminished (9 genes). Genes

uniquely influenced by response to treatment included several that we have previously identified

as being important predictors of treatment response, including IFIT1, DUSP1, GIP3, and LAP3

(Table 3-3B).

87

Table 3-3B: Genes affected by response status

cloneID LOCUS Symb gene Name Hepc/Nor p.value p.response

754047 23097 CDC2L6 cell division cycle 2-like 6 (CDK8-like) 1.73 1.00E-004 2.30E-007

52905 1843 DUSP1 dual specificity phosphatase 1 0.52 1.00E-004 3.59E-007

325364 3434 IFIT1 interferon-induced protein with tetratricopeptide repeats 1 1.92 1.00E-004 7.92E-007

324912 2537 G1P3 interferon, alpha-inducible protein (clone IFI-6-16) 2.64 1.00E-004 4.24E-006

222041 1026 CDKN1A cyclin-dependent kinase inhibitor 1A (p21, Cip1) 2.21 1.00E-004 2.40E-005

485870 55601 FLJ20035 hypothetical protein FLJ20035 2.37 1.00E-004 4.17E-005

156283 EST EST EST 2.21 1.00E-004 0.001067321

26873 55752 SEPT11 septin 11 1.50 1.00E-004 0.002692481

23778 5311 PKD2 polycystic kidney disease 2 (autosomal dominant) 0.53 1.00E-004 0.002839332

428195 51614 SDBCAG84 serologically defined breast cancer antigen 84 1.48 1.00E-004 0.003126268

489284 147184 MGC21518 hypothetical protein MGC21518 1.54 1.00E-004 0.00365049

24477 23327 NEDD4L neural precursor cell expressed, developmentally down-regulated 4-like 1.76 1.00E-004 0.004892418

232802 6296 SAH SA hypertension-associated homolog (rat) 0.66 1.00E-004 0.006133791

276914 2033 EP300 E1A binding protein p300 1.72 1.00E-004 0.006370976

277842 7175 TPR translocated promoter region (to activated MET oncogene) 0.62 1.00E-004 0.007077425

487534 51056 LAP3 leucine aminopeptidase 3 1.45 1.00E-004 0.010975454


485859 64108 IFRG28 28kD interferon responsive protein 1.52 1.00E-004 0.01850426

266045 3428 IFI16 interferon, gamma-inducible protein 16 1.52 1.00E-004 0.027025261

238669 6542 SLC7A2 solute carrier family 7 (cationic amino acid transporter, y+ system) 0.62 1.00E-004 0.028244729

131405 567 B2M beta-2-microglobulin 2.21 1.00E-004 0.035198177

380548 148534 FLJ31842 hypothetical protein FLJ31842 0.60 1.00E-004 0.041631371

240054 10216 PRG4 proteoglycan 4 0.54 1.00E-004 0.043780804


EST, expressing sequence tag; Hepc/Nor, ratio of the gene expression level in CHC-infected liver to normal liver;

P value, P value of HepC/Nor; P response, P value for the factor response.

88

“Genotype” is a surrogate for a viral/host response:

The above results suggest that as a variable, ―response to treatment‖ identifies two underlying

viral/host responses, manifest at the level of gene expression, that influence treatment outcomes.

If so, then ―genotype‖ may be a surrogate for ―response status,‖ and the genes that we found to

be influenced by ―genotype‖ should segregate genotype 1 patients into ―responder‖ and

―nonresponder‖ samples. An unbiased approach to this question is to perform a hierarchical

cluster analysis of all genotype 1 samples using the 17 genes identified in the first ANOVA

analysis, above. As shown in Figure 3-3, this analysis results in two clusters: one is significantly

enriched for responder patients (30/40) and the other for nonresponder patients (11/16) (p<0.005,

Fisher‘s exact test). These results support the contention that genotype is a surrogate for an

underlying host/viral response associated with treatment outcomes.

89

Figure 3-3. Hierarchical cluster plot of CHC genotype 1 patients segregated by the 17 genes

specific to “genotype”

Figure 3-3. Hierarchical cluster plot of genotype 1 patients segregated by ―genotype‖-specific genes. The 17 genes

shown on the right of the plot were significantly associated with ―genotype/disease‖ using the ANOVA multivariate

model. There are 2 main clusters, with nonresponders being significantly enriched in one and responders in the other

(see Results section).

90

ISG15 protein expression defines cell-specific activation in nonresponder vs responder liver

tissue:

Having determined that the ―high ISG‖ nonresponder phenotype is a robust and independent

determinant of a CHC patient‘s likelihood to respond to treatment, we wanted to define the

cellular source of the phenotype. We asked whether the ―high ISG‖ pattern derived from

hepatocytes, indicating a close interaction with the virus, or immune cells, which might indicate

a deficiency in the immune response. We examined the cellular expression of ISG15, one of the

genes highly predictive of treatment response, even alone. ISG15 mRNA is consistently

upregulated in the liver tissue of nonresponder patients, in our lab and others,17-19

and one of the

regulators of ISG15 protein conjugation, USP18, plays an important role in the anti-HCV effect

of IFN.29

Thus, ISG15 protein expression may reveal important aspects of the host response to

CHC infection.

Immunohistochemical studies of the 31 CHC pre-treatment liver biopsies used in our original

description of the ―high ISG‖ phenotype16

were performed using anti-ISG15 pAb (Methods and

Materials). Hepatocyte, macrophage and lymphocyte staining was evaluated for all biopsies as

outlined above. Hepatocytes and macrophages were identified by their typical morphological

appearance. While there were no differences in lymphocyte staining across R and NR samples

(data not shown), there was a consistent and marked difference in hepatocyte and macrophage

ISG15 staining. Treatment nonresponders had more hepatocellular staining, treatment responders

had more macrophage staining (Figure 3-4A and 3-4C); a similar trend was observed in studies

of immunostaining for MxA - another ISG that we found to be differentially upregulated in NR

91

liver tissue (Figure 3-4B).16

There was no association between degree of fibrosis and ISG

(ISG15, MxA) distribution (data not shown).

92

Figure 3-4: ISG activation in different cell types in the pretreatment livers of R and NR

A

4A. Quantitation of hepatocyte and macrophage expression of ISG15 protein in pretreatment liver biopsies of R

(n=15) and NR (n=16) patients. All slides were scored in a blinded fashion from 0 (no cell staining) to 3 (all cells

staining). The Y-axis is the average score for a given response status (R or NR) presented as mean ± SD.

B

Chen et al. Figure 4B

0

0.5

1

1.5

2

2.5

3

3.5

NR R

Hepatocyte

macrophage

** P<0.001

* P=0.0036

4B. Quantitation of hepatocyte and macrophage expression of MxA protein in pretreatment liver biopsies of R

(n=15) and NR (n=16) patients. All slides were scored in a blinded fashion from 0 (no cell staining) to 3 (all cells

staining). The Y-axis is the average score for a given response status (R or NR) presented as mean ± SD.

93

Chen et al. Figure 3-4C

3-4C. representative ISG15 immunohistochemistry. A. Representative liver biopsy from NR patient

immunostained for ISG15 showing predominantly hepatocellular pattern of immunoreactivity (magnification 50x);

B) Liver biopsy from same NR patient as in A), immunostained for ISG15, showing detail of hepatocellular staining

at high power (magnification 400x); C) Representative liver biopsy from R patient, immunostained for ISG15,

showing macrophage pattern of immunoreactivity (magnification 50x); D) Liver biopsy from same R patient as in

C), immunostained for ISG15, showing detail of macrophage staining in high power (magnification 400x).

94

Chen, et al. Figure 3-4D

3-4D. Macrophage-specific staining.

A) Liver biopsy from R patient, immunostained for ISG15, showing typical macrophage pattern of

immunoreactivity in sinusoidal lining cells (magnification 400x);

B) Liver biopsy from the same patient as in A), immunostained for SMA, showing no reaction in sinusoidal lining

cells (magnification 400x);

C) Liver biopsy from same patient, immunostained for CD68, showing immunoreactivity in sinusoidal lining cells

similar in morphology and location to the ISG15 immunoreactive cells in A) and confirming that the ISG15

immunostaining is specific to macrophages (magnification 400x).

95

In order to eliminate the possibility of nonspecific ISG15 antibody staining we performed a

quenching experiment. The binding of anti-ISG15 pAb to recombinant ISG15 protein was

quenched when the antibody was pre-incubated with recombinant ISG15 (Figure 3-5A).

Incubation of purified ISG15 protein (2μg/ml) with antibody specific to MxA had no effect on

the binding of MxA to CHC liver tissue (Figure 3-5B). However, purified ISG15 protein

eliminated binding of ISG15-specific antibody to CHC liver tissue, confirming the specificity of

our findings (Figure 3-5B).

96

Figure 3-5. ISG15-specific immunohistochemical staining:

5A

5A. Recombinant ISG15 protein attenuates ISG15 antibody binding in vitro in a dose-dependent manner. Shown is a

Western blot for ISG15: 10ng of purified human ISG15 in each of the 6 lanes was separated on SDS-PAGE gel and

transferred onto nitrocellulose membrane. The membrane was then cut into 6 individual lanes and incubated with a

mixture of mouse anti-human ISG15 polyclonal antibody (1:300) and ISG15 purified protein (ng/ml) 0 (lane1), 2

(lane 2), 20 (lane 3), 200 (lane 4), 2000 (lane 5), and 4000 (lane 6) for 2 hrs. Note that ISG15 signal was

successfully attenuated (quenched by) the higher amounts of purified ISG15 protein.

97

5B

5B. Confirmation of ISG15-specific staining in immunohistochemical analysis

A) Liver biopsy from R patient, immunostained using mAb for MxA, showing typical macrophage pattern of

immunoreactivity (magnification 200x);

B) Liver biopsy from the same R patient as in (A), first quenched with ISG15 protein (2μg/ml) and then

immunostained with the MxA antibody, showing persistence of immunoreactivity and indicating no cross-reaction

between MxA antibody and ISG15 protein (magnification 200x);

C) Liver biopsy from NR patient, immunostained for ISG15, showing typical hepatocellular pattern of

immunoreactivity (magnification 200x);

D) Liver biopsy from same NR patient as in C), first quenched with ISG15 protein(2μg/ml) and then immunostained

for ISG15, showing loss of immunoreactivity and indicating no cross-reaction between the ISG15 antibody and

MxA or other protein (magnification 200x).

98

We next confirmed the cell types identified above. Resident cells within the liver sinusoidal

spaces are generally Kupffer cells (macrophages) and endothelial cells, but activated stellate

cells may be confused for Kupffer cells.30

We therefore performed IHC using CD68 as a

macrophage marker, and smooth muscle actin (SMA) as a marker for activated stellate cells. As

shown in Figure 3-4D, cells that reacted with antibody specific to ISG15 conformed in

morphology and location to cells that stained with antibody specific to CD68, but did not express

SMA. These results confirm that Kupffer cells are a source of the ISG15 signal.

99

Discussion:

Although the molecular mechanisms involved in IFN resistance in CHC patients who do not

respond to treatment are not well understood, the interaction between viral and host factors plays

a critical role. In this study we present evidence that chronic infection with HCV leads to two

different states at the level of hepatic gene and protein expression: one, the ―high ISG‖

nonresponder phenotype, is associated with high level ISG expression in hepatocytes, the other,

the ―low ISG‖ responder phenotype, has more ISG expression in tissue macrophages. Far from

being a surrogate of clinical or viral factors, our data suggest that this pattern is a strong and

independent predictor of treatment outcome, that the principal source of the ―high ISG‖ signal in

the nonresponder phenotype is the hepatocyte, and that CHC infection is best conceived of in

terms of two patterns of cellular activation giving rise to two different patterns of gene

expression.

Using a larger cohort of patients with CHC than previously,16

we could perform classifier

analyses and seek influences of individual variables on gene expression levels. The similarity in

results obtained with different classifiers (Table 3-2) suggests real biological differences that are

independent of the method used for classification. The original 18-gene set is quite accurate in

predicting who will respond (e.g. positive prediction value for CART, PPV=0.96), but performs

less well for no response (e.g. negative prediction value for CART, NPV=0.66). Similar rates of

prediction were observed using real-time PCR data for the levels of expression of the 18 genes

(data not shown). Taken together, we have observed similar predictive results across two

independent platforms (microarray, real-time PCR) and four independent classifier analyses,

100

arguing that the ISG-dominated gene signature reflects real biological differences between

responder and non-responder groups. In fact, the nonresponder signature was a more powerful

predictor of treatment outcomes than any clinical variable included in our study, even in an

integrative logistic regression model (data not shown).

A similar influence was found in our multivariate analysis of the effect of individual variables on

gene expression values. We performed two analyses: first without and then with ―response to

treatment‖ as a variable (a full description of results and methodology is found on our server:

http://142.150.56.35/~LiverArrayProject2/home.html (user name: lab; Password:

samatalw41). Viral genotype influenced the greatest number of genes in the first analysis, but

―response to treatment‖ influenced more genes in the second analysis with a diminished effect of

genotype. When all genotype 1 samples were sorted by hierarchical cluster analysis, using the

genes most influenced by ―genotype‖ in the first analysis, two clusters resulted: one was

significantly enriched for nonresponders, and one for responders. In fact, we could not identify

any consistent differences between genotype 2/3 patients and genotype 1 patients who responded

to treatment: at the level of gene expression they behave similarly (data not shown). Considered

together, these data suggest that hepatic gene expression is not a surrogate for genotype, but that

there is an independent and consistent difference in how patients who are predisposed to

treatment failure respond to the chronic infection at the level of gene expression.

The fact that the viral genotype is not directly causal of response is supported by other studies

that implicate the host in this process. For example, single nucleotide polymorphisms (SNPs) in

http://142.150.56.35/~LiverArrayProject2/home.html

101

the interferon-stimulated genes for IFNγ, SOCS3, OAS, MxA IL10 and, most-recently, IL28B,

have been linked to treatment response.14,31,32,33

. In the virus, mutations in the interferon

sensitivity-determining region (ISDR) of the NS5a protein correlate with viral load even within

individual viral subtypes.34

In a recent study increased levels of hepatic SOCS3 expression

correlated with nonresponder status regardless of genotype (genotype 1 responders had similar

levels of SOCS3 expression as genotype 2/3 responders).35

Pretreatment hepatic SOCS was the

most powerful predictor of sustained viral response, even when compared to genotype, by

logistic regression analysis. Since SOCS3 is an ISG, this data is consistent with our own, in

which increased expression of a subset of ISGs predicts treatment response and is independent of

genotype alone (Ref 16 and current study). One conclusion of these combined observations is

that the responder ―state of cellular activation‖ is the most-likely outcome of infection with

genotypes 2 and 3 and a less-likely outcome of genotype 1 infection.

A number of the genes that differ between Genotype 1 R and NR patients are ISGs, such as

OAS3, LAP3, DUSP1, and RPS28. These genes are part of our previously identified predictive

gene set for discriminating SVR and NR.16

Although these results suggest a difference in IFN

response, we were unable to find any consistent differences in a survey of IFN-signaling related

gene expression, such as IFNs, their receptors, Jak1, TLR3, A20, and IRF3 (real-time PCR, data

not shown). Other groups have described changes in some of the signaling molecules important

to IFN signaling. Asahina et al examined the baseline expression levels of cytoplasmic viral

sensors and related regulators involved in innate immunity in 74 CHC liver samples. Up-

regulation of RIG-I and MDA5, and down-regulation of Cardif, were associated with NR

patients.36

However, these differences in molecules relevant to IFN signaling may not explain the

102

differences we and others have observed in ISG expression in CHC liver tissue, where IFN levels

are variable but generally low.37

Our immunohistochemical data describes both the source of the ISG ―signal‖ in the

nonresponder phenotype, and identifies a new pattern of cellular activation that correlates with

treatment response: hepatocellular ISG15 and MxA expression in treatment nonresponders,

macrophage ISG15 and MxA expression in treatment responders. This result corroborates

previous work demonstrating a predominantly hepatocellular pattern of staining for the MxA in

treatment nonresponders.26

Our finding that ISG15 and MxA ISG expression are more often

expressed in the macrophages of treatment responders than nonresponders suggest that

macrophages may be more strongly activated in responders. When normal liver tissue is

compared to pretreatment liver biopsies from 10 genotype 1 responders and 10 genotype 1

nonresponders, there is a trend to increased mRNA expression for TNFα, IFNγ, IL1α, IFNα1,

IFNAR2, and IFNβ in responder liver tissue (Supplemental Data). CHC infection leads to

discrete patterns of cellular activation that are reflected in gene expression profiles and that have

profound implications for treatment response.

The different patterns of cell activation identified in this report define a new way of looking at

chronic HCV: as a two-state condition defined by relative macrophage and hepatocyte activation.

The mechanisms underlying this differential state are unclear, but are likely not due to STAT1

signaling. In a recent study pretreatment NR liver tissue had only weak hepatocyte phospho-

STAT1 staining.38

Post-treatment, phospho-STAT1 levels were increased in macrophages, but

103

not consistently in hepatocytes. By contrast, responder liver tissue had little-to-no phospho-

STAT1 staining pre-treatment, but strong hepatocyte staining post-treatment. If this study is

validated, it would suggest that STAT1 is not the driving force behind gene expression prior to

treatment.

On the other hand, most of the ISGs that we have found to be differentially regulated in the liver

tissue of nonresponders16

are linked by virtue of IRF3 sites in their promoter regions

(bioinformatics analysis, not shown). Intriguingly, a similar pattern of IRF3-dependent ISG

expression was seen in response to herpes simplex virus replication in human embryonic lung

cells.39

Whether HCV replication has a similar ability to stimulate IRF3 in nonresponders should

be the focus of future work.

Overall, our data suggests that tissue macrophages in nonresponders are relatively impaired. A

number of studies have described aberrant macrophage function in chronic HCV, possibly

resulting from direct interaction with viral particles. Peripheral blood monocytes from HCV-

infected patients preferentially express IL10 when stimulated by recombinant HCV particles –

the IL10 may suppress T cell responses and inhibit responses to TLR3 and TLR4 ligands. 40,41

HCV core protein can inhibit TLR4-induced production of IL12 by macrophages.42

Although

viral loads are equivalent in the responders and nonresponders in our study, different patterns of

HCV replication in the infected liver might contribute to different patterns of macrophage

activation and functional impairment.

104

In summary, R and NR patients can be distinguished based on the gene expression profiles.

These profiles are independent of viral genotype or any other viral or clinical factor examined,

and do not reflect, as we predicted previously, graded expression of these genes in a single cell

type, but rather markedly different patterns of cellular activation in liver tissue chronically

infected by HCV. The data strongly suggest that treatment responders and nonresponders differ

fundamentally in their response to chronic HCV infection. The majority of HCV research is

directed at defining how HCV affects a single cell (usually the hepatocyte); future work should

be directed at defining how the hepatocyte and macrophage cell types in the liver interact in

chronic HCV and during treatment of the infection.

105

Supplementary Data: Real-time PCR analysis of responders vs nonreponders:

Introduction:

Our data suggests that tissue (liver) macrophages play an important role in mediating immune

responses that impact on response to treatment. To test this hypothesis, we performed a real-time

PCR analysis that examined the mRNA expression of selected inflammatory and immune

modulators in the pre-treatment liver tissue of responders and nonresponders. Liver biopsies

from 10 R and 10 NR pre-treatment liver biopsies were evaluated; all patients were infected with

HCV genotype 1.

Real-Time Polymerase Chain Reaction

Two-step real-time polymerase chain reaction (PCR) was performed after reverse transcription

of 2µg of total RNA with 2 µg pd(N)6-random hexamer primer(Amersham, Oakville, Ontario,

Canada). The resulting complementary DNA was used as a template for real-time PCR

quantification with the QuantiTect SYBR PCR Kit (Qiagen), and real-time PCR (normalized to -

actin) was performed using the DNA Engine Opticon 2 cycler (MJ Research, Reno, NV). For

primers, see Table 3-4.

106

Table 3-4: real-time PCR primers

Gene name Forward primer Reverse primer Amplicon length (bp)

100

121IFN 109

109

IFN 1 127

IFN 161

Results:

As shown in Figure 3-6, genes for inflammatory mediators, such as TNFα, IL-1 and IFNγ,

tended to be more highly expressed in the liver tissue of treatment responders than

nonresponders. This data corroborates the immunohistochemical impression that macrophage

activation is more pronounced in the liver tissue of responders than in nonresponders. While this

trend will need to be validated in further studies, it does support the finding that macrophage

activation is more pronounced in the liver tissue of eventual responders than in nonresponders,

even within Genotype 1 patients. The finding that IFNAR2 is increased more in responders than

nonresponders adds to previous reports that demonstrated that relative to responders,

nonresponders have decreased expression of this component of the IFN receptor.1,2

107

Figure 3-6: Real-time PCR for inflammatory mediators in normal, R and NR livers

Chen et al. Supplementary Figure 1

0.0000

0.0005

0.0010

0.0015

0.0020

0.0000

0.0002

0.0004

0.0006

0.0008

0.0010

0.0012

0.0014

0.000

0.002

0.004

0.006

0.008

0.0000

0.0002

0.0004

0.0006

0.0008

0.0010

0.0012

0.0014

0.000

0.001

0.002

0.003

0.004

0.000

0.002

0.004

0.006

0.008

* **

TNFα IFNAR2 IFNγ

IL1α IFNα1 IFNβ

N R NR N R NR N R NR

N R NR N R NR N R NR

Real-time PCR of indicated genes in normal liver tissues (n=10, blank), pretreatment liver tissues from HCV

treatment responders (n=10, grey), pretreatment liver tissues from HCV treatment nonresponders (n=10, black). All HCV liver biopsies were taken prior to initiation of treatment with PegIFN/Rib. Responder and nonresponder liver

tissue had similar viral loads, and all HCV biopsies were obtained from Genotype 1 patients.

108

References for supplementary data:

1. Fujiwara D, Hino K, Yamaguchi Y, et al. Type I interferon receptor and response to interferon

therapy in chronic hepatitis C patients: a prospective study. J Viral Hepat. 2004 Mar;11(2):136-

40.

2. Morita K, Tanaka K, Saito S, et al. Expression of interferon receptor genes (IFNAR1 and

IFNAR2 mRNA) in the liver may predict outcome after interferon therapy in patients with

chronic genotype 2a or 2b hepatitis C virus infection. J Clin Gastroenterol. 1998;26(2):135-40.

109

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Leukoc Biol. 2007;82(6):1407-19.

113

Chapter 4: Microarray screen identifies in an unbiased way the ISG15/USP18 pathway to be

involved in IFN resistance in HCV infected patients

From 19,000 genes tested, I found a few hundred genes whose expression levels were altered in

HCV chronically-infected liver tissues, and 18 genes that showed differential expression between

Responders (R) and Non-responders (NR). 3 out of these 18 genes are involved in the

ISG15/USP18 ubiquitin-like signaling pathway (only 5 genes in this pathway). Therefore, this

pathway might play an important role in IFN resistance in those patients who do not respond to

treatment.

To further explore the role of the ISG15/USP18 pathway in HCV production, I used the HCV in

vitro culture model to address the effects of USP18 and ISG15 on HCV production.

Chapter 4-1: The role of USP18 in HCV production

The work ―Silencing of USP18 Potentiates the Antiviral Activity of Interferon Against

Hepatitis C Virus Infection” was published in Gastroenterology 2006;131(5):1584-1591

My role in this study: participating in experimental design, performing part of the experiments,

acquisition of the data, summarizing/analyzing data I generated and writing part of the paper

Reprinted from Gastroenterology, Vol 131, Randall, et al. Silencing of USP18 Potentiates the

Antiviral Activity of Interferon Against Hepatitis C Virus Infection. Page 1584-1591, Copyright

(2006), with permission from Elsevier.

114

Silencing of USP18 Potentiates the Antiviral Activity of Interferon Against Hepatitis C

Virus Infection

Glenn Randall, ‡

, Limin Chen§,

, Maryline Panis , Andrew K. Fischer‡, Brett D. Lindenbach ,

Jing Sun§, Jenny Heathcote

¶, Charles M. Rice , Aled M. Edwards

§, , # and Ian D. McGilvray

,

#Structural Genomics Consortium, University of Toronto, Toronto, Canada

‡Department of Microbiology, University of Chicago, Chicago, Illinois

Center for the Study of Hepatitis C, Rockefeller University, New York, New York

Department of Surgery, University of Toronto, Toronto, Canada

Department of Medical Genetics and Microbiology, University of Toronto, Toronto, Canada

§Banting and Best Department of Medical Research, University of Toronto, Toronto, Canada

¶Department of Medicine, University of Toronto, Toronto, Canada

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115

Abstract

Background & Aims: Modulation of the host innate immune response is an attractive means of

inhibiting hepatitis C virus (HCV) replication. Having previously determined that expression of

the interferon-sensitive gene (ISG)15 protease USP18 is increased in the liver biopsy specimens

of patients who do not respond to interferon (IFN)-alfa therapy, we hypothesized that USP18

might hinder the ability of IFN to inhibit HCV replication. Methods: The role of USP18 in IFN

antiviral activity was examined using an in vitro model of HCV replication that reproduces the

full viral life cycle. USP18 was silenced specifically using small inhibitory RNAs (siRNAs), and

the dose response of HCV replication and infectious virus production to IFN-alfa was measured.

Results: The siRNA knockdown of USP18 in human cells consistently potentiated the ability of

IFN to inhibit HCV-RNA replication and infectious virus particle production by a factor of 1–2

log10. USP18 knockdown also resulted in a number of cellular changes consistent with increased

sensitivity to IFN. Decreasing USP18 expression led to increased cellular protein ISGylation in

response to exogenous IFN-alfa, prolonged tyrosine phosphorylation of signal transducer and

activation of transcription (STAT1), and a general enhancement of IFN-stimulated gene

expression. Conclusions: These data suggest that USP18 modulates the anti-HCV type I IFN

response, and is a possible therapeutic target for the treatment of HCV infection.

116

Abbreviations:

EC50, median effective concentration; GAPDH, glyceraldehyde-3-phosphate dehydrogenase;

IFN, interferon; ISG, interferon-sensitive gene; PCR, polymerase chain reaction; siIRR,

irrelevant small inhibitory RNA; siRNA, small inhibitory RNA

117

Introduction

Hepatitis C virus (HCV) is a serious public health problem. More than 170 million persons

worldwide are infected by the virus, which can lead to end-stage liver disease and hepatocellular

carcinoma.1 The best antiviral therapy at present is a combination of pegylated interferon (IFN)-

2-alfa and ribavirin, but almost half of all patients do not respond to this costly and often quite

morbid treatment.2,3

Given the widespread nonresponse to exogenous IFN-alfa, novel therapeutic

approaches that either augment or complement the antiviral activity of IFN-alfa would be very

beneficial.

We recently showed that patients who do not respond to pegylated IFN-2-alfa and ribavirin show

a consistent alteration in their pretreatment hepatic gene expression.4 When compared with

responder liver tissue, nonresponders have increased expression of a number of IFN-sensitive

genes. The expression of a subset of 8 genes accurately predicted treatment response in more

than 90% of patients independent of viral load, genotype, or hepatic fibrosis. Two of the 8 genes,

USP18 and IFN-sensitive gene (ISG)15, are linked biochemically. ISG15 is a ubiquitin-like

molecule that is posttranslationally attached to the lysine residues of more than 150 target

proteins.5,6

Protein targets include other IFN-stimulated genes, in addition to proteins in a diverse

set of unrelated pathways. The function of ISG15 conjugation remains elusive. USP18/UBP43 is

a ubiquitin-specific protease that cleaves the ubiquitin-like (and IFN-induced) ISG15 protein

from its cellular targets in vitro.7 Together these data suggested that nonresponder patients have a

disordered host IFN response, and correlate the up-regulation of USP18 with nonresponse to IFN

treatment.

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Multiple lines of evidence previously have linked ISG15, USP18, and the cellular response to

IFN. Both genes are induced by type I IFN in many cell types,8-10

and cells isolated from USP18

knockout mice are hypersensitive to IFN, with prolonged Jak–Stat signaling.11

These proteins

also have been linked to the innate immune response to viruses. Overexpression of ISG15

enhances the antiviral activity of IFN against human immunodeficiency virus and Sindbis virus

replication in vitro.12-14

Influenza B virus NS1 protein binds ISG15 and prevents host protein

ISGylation, a function that correlates with influenza B resistance to IFN.15

Based on these data,

we hypothesized that USP18 may be a critical determinant of the human IFN antiviral response

to HCV.

Until recently, hypotheses about mechanisms of HCV immunity have been difficult to address

because a reliable cell culture model for the propagation of HCV was not available. However, we

recently developed an in vitro HCV model that reproduces the complete HCV replication cycle.16

The viral genome is a chimeric HCV genotype 2a sequence. Its replication is robust ( 105

infectious units/mL) and is neutralized by antibodies to the viral glycoprotein E2, or a soluble

form of a cellular receptor for HCV, CD81. Importantly, replication is sensitive to a number of

viral inhibitors, including IFN. By using this model, we were able to address the role of USP18

in the antiviral functions of IFN against HCV.

Materials and Methods

Cells and HCV FL-J6/JFHVirus

Huh-7.5 cells are a subline derived from Huh-7 hepatoma cells.17

HCV FL-J6/JFH is a full-

length genotype 2a sequence that produces the full replication cycle in cell culture.16

It is a











119

chimera containing the JFH 5′ nontranslated region,18

the J6 core through NS2 genes,19

and the

JFH NS3 through 3′ nontranslated region. Cells were maintained in Dulbecco‘s modified Eagle

media supplemented with nonessential amino acids and 10% fetal bovine serum. They were

maintained at 37°C in 5% CO2.

RNA Interference Assay

USP18 small inhibitory RNAs (siRNAs) were obtained as follows (for each, the sense strand

sequence is described): 4 USP18 siRNAs: siUSP18-a, 5′-GGAAUUCACAGACGAGAAAUU;

siUSP18-b,5′GGAAGAAGACAGCAACAUGUU;siUSP18-c, 5′

GGGAAGACAUCCAGUGUACUU; and siUSP18-d, 5′CCAGGAGUUAACACCCUGGUU.

The irrelevant siRNA (siIRR) is 5′-ggcgcuuguggacauucugTT. Chemically synthesized RNA

oligos were prepared as recommended by the manufacturer (Dharmacon, Lafayette, CO) and

transfected as described previously.20-22

One nanomole of RNA duplexes in annealing buffer

(100 mmol/L potassium acetate, 30 mmol/L HEPES-KOH pH 7.4, 2 mmol/L magnesium acetate)

was electroporated into 2.5 × 106 Huh-7.5 cells (5 pulses of 900 V for 99 μs, with 1-s intervals

on a BTX electroporator [Harvard Apparatus, Inc, Holliston, MA]). Thirty hours after

electroporation, cells were treated with IFN-alfa (0–1000 U) for 15 hours, washed, infected with

HCV FL-J6/JFH for 6 hours, rinsed, and overlaid in media lacking IFN-alfa. RNA and protein

were harvested for analysis of ISG expression. Two days after infection, cells were harvested for

assessment of HCV replication (RNA and infectious particles). The effect of USP18 siRNAs on

IFN activity against previously infected Huh-7.5 cells was tested as follows. Huh-7.5 cells were

infected for 6 hours with 2 multiplicities of infection of J6-JFH HCV, then electroporated with

irrelevant or USP18 siRNAs as per the standard protocol. Cells were seeded into 96 wells,






120

maintained, and then treated with indicated amounts of IFN from 24 to 48 hours after infection.

IFN-containing media then was replaced with Dulbecco‘s modified Eagle media + 10% fetal

bovine serum. Cellular RNAs were harvested 72 hours after infection for quantification of HCV

replication.

Quantification of Infectious HCV

Virus titers were determined by limiting dilution analysis as described previously.16

Supernatants

from infected cells were collected and diluted 10-fold in media. Eight thousand Huh-7.5 cells

were inoculated with virus titrations in 96-well plates (8 wells/dilution) and maintained for 3

days. Cells were washed in phosphate-buffered saline (PBS), fixed in methanol at −20°C, and

blocked in PBS/1% bovine serum albumin/.2% milk followed by 3% H2O2. Cells were incubated

with monoclonal antibody to HCV NS5A (9E10) at 1:200, followed by mouse-horseradish

peroxidase (Vector Impress, Burlington, ON, Canada). Cells then were exposed to 3,3′-

diaminobenzidine tetrahydrochloride reagent (DAKO, Carpinteria, CA). HCV-positive wells

were counted and the 50% infectious dose was calculated by the method of Reed and Muench.23

RNA Quantification

Total RNA was harvested and purified with 96-well RNA-easy columns as recommended by the

manufacturer (Qiagen, Mississauga, ON, Canada). For cellular RNAs, 2μg of DNaseI-treated

total RNA was reverse transcribed with Superscript II (Invitrogen, Carlsbad, CA) for 2 hours at

42°C. Complementary DNA (cDNA) synthesis was primed with AnCT primer (Invitrogen). The

reverse transcriptase was heat inactivated at 95°C for 5 minutes. A total of 1/15 of the cDNA mix

was mixed with an equal volume of 2× Sybr Green master mix (Qiagen). Polymerase chain



121

reaction (PCR) was performed using the following forward and reverse primers: ISG15: 5′-

CGCAGATCACCCAGAAGATT and 5′-GCCCTTGTTATTCCTCACCA, IFN-induced protein

with tetratricopeptide repeats: 5′-GCAGCCAAGTTTTACCGAAG and 5′-

GCCCTATCTGGTGATGCAGT; and 2′-5′-oligoadenylate synthetase 3: 5′-

GTCAAACCCAAGCCACAAGT and 5′-GGGCGAATGTTCACAAAGTT. Values were

normalized to that of β-actin, amplified with forward and reverse primers 5′-

TGGACTTCGAGCAAGAGATGG and 5′-GGAAGGAAGGCTGGAAGAGTG. PCR

conditions were as follows: 94°C for 10 minutes, (94°C for 45 s, 56°C for 45 s, 72°C for 1 min)

× 45 cycles. For HCV-RNA quantitation, 50 ng of total RNA was reverse transcribed and

amplified with HCV-specific primers 5-TGA GTG TCG TAC AGC CTC CA and 5′-ACG CTA

CTC GGC TAG CAG TC using Platinum Quantitative reverse-transcription PCR Thermoscript

One-step System (Invitrogen, Life Technologies). A 100-ng aliquot of total RNA was used to

quantify HCV-specific RNA levels using an ABI Prism 7700 sequence detector (Applied

Biosystems, Foster City, CA). PCR conditions were as follows: 50°C for 2 minutes, 50°C for 30

minutes, 95°C for 10 minutes (95°C for 15 s, 50°C for 40 s, 72°C for 30 s) × 50 cycles.

Glyceraldehyde-3-phosphate dehydrogenase detection mix from Applied Biosystems (VIC-

MGBNFQ) was amplified as follows: 50°C for 2 minutes, 50°C for 30 minutes, 95°C for 10

minutes, (95°C for 15 s, 60°C for 1 min) × 40 cycles and used for normalization. Results were

analyzed with SDS 1.9 software from Applied Biosystems.

Statistical Analysis

HCV replication data were analyzed with Prism software (San Diego, CA). Values from the

indicated number of replicates for each sample were entered and a dose-response curve was

122

generated by using the nonlinear sigmoidal dose-response parameter. The median effective

concentration (EC50) values were calculated from these generated curves. The statistical

significance of the different curves then was generated using an F test, which is appropriate for

testing the goodness-of-fit of 2 models. This is assessed by the sum of squares, adjusting for

difference in the number of degrees of freedom. For Figures in which the P value is discussed,

the siIRR data set contains 23 values, whereas the siUSP18 data set contains 22 values. The

resulting P value was less than the lower limit (P < .0001).

Immunoblot Analysis

At various times after virion exposure, Huh-7.5 cells were detached, washed once in PBS, then

resuspended in 1× sodium dodecyl sulfate lysis buffer (50 mmol/L Tris pH 6.8, 2% sodium

dodecyl sulfate, .1% bromphenol blue dye, 10% glycerol, and 100 mmol/L β mercaptoethanol)

and heated to 95°C for 5 minutes. Lysates were passed through a 20G needle 10 times. Lysates

prepared from 100,000 cells were separated on sodium dodecyl sulfate–polyacrylamide gel

electrophoresis and transferred to a polyvinylidene difluoride membrane (Millipore, Billerica,

MA). Blots then were probed with the indicated antibodies. Antibodies used in this study include

monoclonal anti-ISG15 antibodies (the kind gift of Dr. E. Borden, Scripps Institute, La Jolla,

CA), monoclonal anti-phospho-STAT1 (Tyr701) (9172; Cell Signaling, Danvers, MA), and

monoclonal anti-STAT1 (9172; Cell Signaling).

Results

USP18 Silencing Enhances the Antiviral Activity of IFN-Alfa in an Infectious HCV Cell

Culture System

123

We have shown previously that both viral (HCV) and cellular (lamin A/C and CD81) RNAs can

be silenced efficiently in Huh-7.5 cells with siRNAs.20-22

By using these conditions, we tested

the role of USP18 in IFN-alfa signaling and antiviral function in an in vitro HCV cell culture

system. siRNAs were first introduced to establish silencing, after which IFN-alfa was added and

the effects of IFN-alfa treatment were determined. USP18 expression was highly induced by IFN

in Huh-7.5 cells treated with irrelevant siRNAs. This induction was dose dependent and is

consistent with USP18 being an IFN-stimulated gene. A pool of 4 USP18-specific siRNAs

(siUSP18) abrogated this effect, resulting in 75%–90% silencing over all doses of IFN-alfa

(Figure 4-1-1A). Each individual USP18 siRNA, targeting a distinct sequence, was effective at

reducing USP18 RNA levels (Figure 4-1-1B).



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Figure 4-1-1. Inhibition of USP18 expression by USP18 siRNAs. (A) Huh-7.5 cells were transfected with siIRR (□)

or USP18-specific (■, siUSP18) siRNAs, maintained for 30 hours, and then treated with the indicated IU of IFN/mL

for 24 hours. RNA was purified and quantified by real-time PCR. USP18 RNAs were normalized to glyceraldehyde-

3-phosphate dehydrogenase (GAPDH) RNA levels and the fold induction of USP18 RNA in response to IFN is

represented. Data are the mean ± SD for duplicate samples and are representative of 3 independent experiments. (B)

Huh-7.5 cells were treated with siIRR, or 4 individual USP18 siRNAs (siUSP18-a–d) for 45 hours. RNA was

purified and USP18 and GAPDH RNAs were quantified. Data were normalized for GAPDH RNA levels and are

represented as a ratio of USP18/GAPDH relative to siIRR-treated cells. Data are the mean ± SD of duplicate samples.

125

We then tested the effects of IFN-alfa with or without USP18 silencing on HCV replication and

infectious virus production in vitro. Huh-7.5 cells were transfected with an irrelevant siRNA, or

USP18 siRNAs (pooled and specific), and treated with IFN-alfa. After IFN-alfa treatment, cells

were infected with J6-JFH HCV and maintained for 2 days. Infectious virus particles were

quantified from cell supernatants (Figure 4-1-2B), while intracellular RNA was extracted and

HCV-RNA levels were determined (Figure 4-1-2A). In irrelevant siRNA-treated cells, HCV

replication was inhibited at an EC50 concentration of 1.6 IU/mL IFN-alfa. This was comparable

with the EC50 in cells that were not treated with siRNAs (.9 IU/mL IFN-alfa, data not shown). In

cells treated with USP18 siRNAs, HCV replication was inhibited at significantly lower IFN-alfa

conditions. The EC50 concentration in USP18 siRNAs was .1 IU/mL IFN-alfa (a 1.2-log10

enhancement of IFN-alfa anti-HCV activity). This change in EC50 was significant by F test (P

< .0001).



126

Figure 4-1-2. USP18 silencing augments the antiviral effects of IFN against HCV infection. Huh-7.5 cells were

transfected with siIRR (■) or siUSP18 ( ), maintained for 30 hours, and treated with the indicated concentrations

of IU IFN/mL. After 15 hours, the cells were infected with HCV for 8 hours, then washed and maintained in media

lacking IFN for 2 days, and HCV replication was measured. (A) Intracellular RNAs were purified and GAPDH and

HCV RNAs were quantified by real-time PCR. (B) Infectious HCV in the supernatants of cells from A was

quantified by limiting dilution assay and the 50% infectious dose/mL was calculated. The percentage of inhibition in

response to IFN relative to untreated cells is shown. Data are the mean ± SD of (A) quadruplicate and (B) triplicate samples. The data are representative of 4 independent experiments.

127

The dramatically enhanced IFN-alfa inhibition of HCV replication in USP18-silenced cells

correlated with decreased production of infectious HCV particles. Infectious virus particles in

cell supernatants were quantified as described in the Materials and Methods section. Viral titers

ranged from 20,000 50% infectious doses/mL in untreated cells, to undetectable viral titers in

cells treated with high levels of IFN. USP18-silenced cells consistently showed enhanced

antiviral activity of IFN-alfa, with a 1.3-log10 enhancement in IFN-alfa anti-HCV activity (Figure

4-1-2B). The effects of IFN-alfa on the production of infectious HCV were similar to, if not

slightly greater than, the effects on HCV-RNA replication. We confirmed that this effect was

specific to USP18 by testing 4 different USP18 siRNAs. All USP18 siRNAs tested consistently

augment the antiviral activity of IFN-alfa (Figure 4-1-3). Despite minor variations in the basal

sensitivity to IFN, each USP18 siRNA had the effect of shifting the IFN dose response and EC50

inhibition of HCV replication. The fact that all USP18 siRNAs show similar phenotypes strongly

argues that the observed enhancement of IFN-alfa antiviral activity is specific to USP18.




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Figure 4-1-3. Multiple USP18 siRNAs enhance the antiviral effects of IFN against HCV. Cells were treated with

siIRR (■), or 4 individual USP18 siRNAs ( , ,♦,● siUSP18-a–d, respectively) for 30 hours, then with indicated

IU IFN/mL for 15 hours. Cells were rinsed, inoculated with HCV for 8 hours, rinsed again, and maintained for 2 days in media lacking IFN, and HCV replication was measured. (A) HCV RNA was quantified by real-time PCR

and normalized to GAPDH levels. (B) Infectious HCV was quantified by limiting dilution assay and the 50%

infectious dose/mL was calculated. The percentage of inhibition relative to untreated cells then was calculated for

each. Data are the mean ± SD of (A) quadruplicate and (B) duplicate samples and are representative of 2

independent experiments.

129

IFN-alfa is a cytokine that can establish an antiviral state in both naive and virally infected cells.

The previous experiments tested the effect of treating naive cells with USP18 siRNAs and IFN

before infection. We next examined whether USP18 siRNAs altered the antiviral activity of IFN

in cells that already are infected with HCV. Huh-7.5 cells were infected with HCV at a

multiplicity of infection of 2, then transfected with USP18 siRNAs, followed by treatment with

IFN-alfa. Viral replication then was quantified as described previously (Figure 4-1-4). IFN-alfa

inhibited ongoing HCV replication in irrelevant siRNA-treated cells (EC50 = .06 IU/mL IFN-

alfa). The antiviral activity was enhanced approximately 30-fold in the presence of USP18

siRNAs (EC50 = .002 IU/mL IFN-alfa). Thus, USP18 siRNAs enhance the antiviral activity of

IFN-alfa cells that are infected before treatment, in addition to priming the antiviral state in

uninfected cells.


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Figure 4-1-4. USP18 silencing augments the antiviral effects of IFN in cells previously infected with HCV. Huh-7.5

cells were infected with HCV at a multiplicity of infection of 2 for 6 hours. Infected cells then were transfected with

siIRR (■) or siUSP18 ( ), maintained for 18 hours, and treated with the indicated concentrations of IU IFN/mL.

After 24 hours, the cells were washed and maintained in media lacking IFN for 2 days, and HCV replication was

measured. Intracellular RNAs were purified and GAPDH and HCV RNAs were quantified by real-time PCR. The

percentage of inhibition in response to IFN relative to untreated cells is shown. Data are the mean ± SD of triplicate

samples. The data are representative of 2 independent experiments.

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Efficient Silencing of USP18 in IFN-Alfa–Treated Huh-7.5 Cells Enhances Jak1-STAT1

Signaling and ISG Expression

We next investigated the mechanism of enhanced IFN antiviral activity in USP18 silenced

human hepatoma cells. Because USP18 is an ISG15-specific protease, decreasing USP18

expression should result in increased ISGylation of cellular proteins. In irrelevant siRNA-treated

cells, free ISG15 increased in response to IFN-alfa treatment, and ISG15–protein conjugates

accumulated in cells treated with high doses of IFN-alfa (Figure 4-1-5A). By contrast, USP18-

silenced cells had abundant ISG15-protein conjugates that accumulated at lower concentrations

of IFN-alfa, and increased unconjugated ISG15. This phenotype is similar to that described for

mouse cells with a USP18 knockout,11

and is consistent with decreased USP18 ubiquitin protease

activity and increased ISG15 expression. The low levels of ISG15-protein conjugates in

irrelevant siRNA-treated cells and their enhanced accumulation in USP18 siRNA-treated cells

confirms that the ISGylation pathway is functional in Huh-7.5 cells and suggests that USP18

negatively regulates protein ISGylation.



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Figure 4-1-5. USP18 silencing enhances protein ISGylation and ISG15 induction by IFN. Huh-7.5 cells were treated as before with either siIRR or siUSP18, then with the indicated IU of IFN/mL. After 24 hours of IFN treatment, (A)

protein lysates were collected, separated, and probed for ISG15 expression, or (B) RNA was purified and ISG15 and

actin RNAs were quantified. RNA levels were normalized and presented as in Figure 4-1-1; the data are

representative of 3 independent experiments. (B) □, siIRR; ■, siUSP18.

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The increased ISG15 protein accumulation in USP18-silenced cells may reflect a general

enhancement in ISG expression. This is supported by the observation that USP18 knockout mice

are hypersensitive to IFN-alfa.11,24

As shown in Figure 4-1-5B, USP18 knockdown resulted in a

substantial (3- to 5-fold) increase in the IFN-alfa induction of ISG15 transcription as determined

by real-time PCR. This effect was not limited to ISG15. IFN-alfa induction of 4 other ISGs—2′-

5′-oligoadenylate synthetase 3, IFN-induced protein with tetratricopeptide repeats 1, Viperin, and

myxovirus resistance 1—also was enhanced after silencing of USP18 (Figure 4-1-6). These

genes were examined because (1) they are ISGs and (2) we previously showed that their

expression is increased in the liver tissue of patients with chronic HCV.4 These data are

consistent with a general enhancement of IFN-alfa–stimulated gene expression in USP18-

silenced cells.






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Figure 4-1-6. General enhancement of ISG induction in USP18-silenced cells. Huh-7.5 cells were treated with either

siIRR (□) or siUSP18 (■) for 30 hours, followed by the indicated IU of IFN/mL for 24 hours. RNA was purified and

quantified by real-time PCR: (A) 2′-5′-oligoadenylate synthetase 3, (B) interferon-induced protein with

tetratricopeptide repeats 1, (C) Viperin, (D) myxovirus resistance 1. RNA levels were normalized to β-actin RNA

levels and expressed as in Figure 4-1-1; the data are representative of 2 independent experiments.

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The enhanced IFN-alfa–stimulated gene expression suggests that regulation of IFN-alfa signaling

has been altered in USP18-silenced cells. We next examined the activation of a critical

component of IFN-alfa signaling, STAT1. On engagement of the IFN-alfa–receptor subunits

IFNAR1 and IFNAR2, the receptor-bound proteins Tyk2 and Jak1 are phosphorylated. This in

turn leads to phosphorylation of STAT1 and STAT2 and their assembly with interferon

regulatory factor (IRF)9 into the transcriptional activation complex ISGF3, leading to activation

of ISGs. We examined the effects of USP18 silencing on the activation of this pathway, and

specifically on STAT1 phosphorylation. In cells transfected with siIRR, STAT1 phosphorylation

is induced within 1 hour of IFN treatment and returns to basal levels by 4 hours. In USP18-

silenced cells, STAT1 is phosphorylated within 1 hour of IFN-alfa treatment; however,

phosphorylated STAT1 remains 15 hours after treatment. Total STAT1 increases after IFN-alfa

treatment (Figure 4-1-7), as would be expected for an ISG. These results indicate that IFN

signaling is initiated normally in USP18-silenced cells, but that there is a defect in the

subsequent negative regulation of IFN signaling. As such, it implicates a role for USP18 in the

negative feedback of IFN signaling, with an outcome of limiting the anti-HCV activity of IFN-

alfa in human hepatoma cells.


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Figure 4-1-7. USP18 silencing prolongs STAT1 activation after IFN treatment. Huh-7.5 cells were transfected with

siIRR or siUSP18, maintained for 24 hours, and treated with 100 IU of IFN/mL. Protein lysates were harvested at

the indicated time, separated electrophoretically, and probed for STAT1-phospho701. Blots then were stripped and

probed for total STAT1 expression.

137

Discussion

In a recent study, we found that expression of USP18 was disordered in the pretreatment liver

tissue of patients with chronic HCV who do not respond to subsequent treatment with pegylated

IFN-2-alfa and ribavirin.4 Specifically, increased USP18 expression correlated with lack of

response to combination therapy. In the present study, we show that USP18 plays an important

role in the anti-HCV–IFN response using an in vitro model of HCV replication that reproduces

the full viral life cycle. The silencing of USP18 altered the cellular response to IFN-alfa,

resulting in more protein ISGylation, prolonged STAT1 activation, and increased expression of

ISGs. The increased biochemical effect of INF-alfa was mirrored by a potentiated inhibition of

HCV RNA and infectious particle production in vitro. The congruence between our in vivo and

in vitro results suggests that USP18 plays an important role in HCV pathophysiology.

USP18 may have a broader role in human disease. Increased ISG15 and/or USP18 expression

correlates with chronic HCV infection and with the clearance of HBV infection in vivo.4, 25-28

In

animal models, USP18 knockout mice are resistant to otherwise fatal intracerebral infection by

lymphocytic choriomeningitis virus and vesicular stomatitis virus. This effect was associated

with decreased viral replication and increased cellular ISGylation by ISG15.29

However, USP18

may not modulate the activity of IFN against all viral infections. In preliminary studies, USP18

silencing in Hela cells led to increased protein ISGylation after treatment with IFN-alfa, but did

not alter measles virus replication (Dr. C. Richardson, unpublished observations).

The mechanism by which USP18 modulates the antiviral activity of IFN remains unclear.

Decreased USP18 expression results in IFN hypersensitivity, and resistance to viral infection in

human cells in this study and in knockout mice.29

Although this correlates with an increase in







138

ISG15 protein conjugates, this phenotype is apparently ISG15 independent. Mice that lack

ISG15 have an intact IFN system and respond normally to viral challenge.30

Similarly, in

preliminary results we have found that siRNAs targeting ISG15 did not alter the anti-HCV

activity of IFN (data not shown). Quite recently, 2 lines of double knockout mice have been

examined: USP18 and ISG15−/− and USP18 and Ube1L−/−. Both mice had similar IFN and

antiviral sensitivity as the parental USP18−/− mice. This suggests that the function of USP18 in

regulating the sensitivity to IFN is ISG15 independent.

In this study we have shown that STAT1 activation is altered in USP18-silenced cells, producing

a phenotype similar to USP18−/− mice. Specifically, STAT1 appears to be activated

(phosphorylated) normally in USP18-silenced cells, but remains active for extended periods of

time compared with normal Huh-7.5 cells. This suggests a defect in the negative regulation of

IFN signaling. Because USP18 is itself induced by IFN-alfa, it appears to be a critical regulator

in a classic negative feedback loop. USP18 would appear to regulate a process upstream of

STAT1 phosphorylation, such as IFN-alfa–receptor turnover or the interactions between the

receptor and signaling molecules. Prolonged STAT1 activation is associated with a general

increase in IFN-stimulated gene expression in USP18-silenced cells. The specific anti-HCV

effectors modulated by USP18 remain to be identified. We have shown that the IFN induction of

at least 5 ISGs is enhanced by USP18 silencing. In this light, it is interesting to note that one of

these, IFN-induced protein with tetratricopeptide repeats 1, has been shown previously to play a

role in inhibiting HCV replication in a replicon model.31

We believe that the question of how USP18 contributes to IFN-alfa antiviral activity in human

HCV is a critical one. Not only is IFN-alfa the major component of antiviral therapy for acute



139

and chronic HCV infection, but nonresponse to IFN-alfa is an important and unresolved issue in

HCV treatment. IFN-alfa has been shown to target multiple stages of viral life cycles, notably

translation, but also viral entry and capsid assembly.32

The HCV infectious cell culture system

used in this study recapitulates the entire viral life cycle, and therefore provides the most

advanced system for studying the antiviral activity of IFN-alfa against HCV. USP18 silencing in

this system produced a robust and consistent enhancement of the ability of IFN-alfa to inhibit

HCV RNA and infectious virus production.

In conclusion, this study extends our observations that USP18 is a predictor of successful IFN-

alfa therapy by showing that USP18 plays a critical role in the antiviral activity of IFN-alfa

against HCV infection in vitro. Currently one of the chief limitations of HCV treatment is the

fact that a large fraction of compliant patients do not respond to exogenous therapy in a sustained

manner. Enhancing the antiviral activity of IFN by modulation of USP18 may represent a

strategy for improving responses to HCV treatment.

Acknowledgement:

The authors thank Victoria Kramer for technical assistance, and Dr. David Grant, University Health Network, for

critical insight with the manuscript. The authors also acknowledge the work of Grant Welstead in Dr. Chris

Richardson‘s laboratory, University of Toronto, for assistance with our preliminary measles virus studies.


140

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ISG15 has been recognized as an anti-viral protein. My gene expression study indicated that

elevated pretreatment ISG15 expression in the liver is associated with treatment non-response

even though ISG15 would be predicted to have anti-HCV activity.

In order to clarify the role of ISG15 in HCV infection, I modulated ISG15 expression and then

tested the effect of these modulations on HCV RNA replication and viral particle production

using the in vitro HCV culture model.

I published this piece of data entitled ―ISG15, a ubiquitin-like interferon stimulated gene,

promotes Hepatitis C Virus production in vitro: Implications for chronic infection and response

to treatment‖ in J Gen Virol. 2010 Feb;91(Pt 2):382-8. Epub 2009 Oct 21.

My role in this study: experimental design, performing experiments, summarizing/analyzing the

data and writing up the paper

Reprinted from J Gen Virol. 2010 Feb;91(Pt 2):382-8

144

ISG15, a ubiquitin-like interferon stimulated gene, promotes Hepatitis C Virus production

in vitro: Implications for chronic infection and response to treatment

Limin Chen1,2

, Jing Sun1, Larry Meng

1, Jenny Heathcote

3, Aled M. Edwards

1,2,4, Ian D.

McGilvray3,5*

From the Banting and Best Department of Medical Research1, Department of Molecular

Genetics2, Departments of Medicine

3, Medical Biophysics

4, and Surgery

5, University of Toronto,

Toronto, Ontario

Keywords: Interferon stimulated gene 15Kda (ISG15), Hepatitis C virus (HCV), ISG15

conjugation (ISGylation), Ubiquitin-E1 like (Ube1L) activating enzyme, siRNA

Running title: ISG15 stimulates HCV production

Grant support: This work was funded by grants from the Canadian Institute of Health Research

(No. 62488 to I.D.M). L.C. was supported by the National Canadian Research Training Program

in Hepatitis C (NCRTP-HepC) and Canada Graduate Scholarship (CGS) from the Canadian

Institute of Health Research.

*Corresponding author:

Ian D. McGilvray MD, PhD

Assistant Professor of Surgery

11C1250 Toronto General Hospital

585 University Avenue

Toronto, Ontario M5G 2N2

Phone: 416 340 4190

Fax: 416 340 5242

e-mail: [email protected]

mailto:[email protected]

145

Abbreviations :

Ube1L: ubiquitin E1 like

HCV: Hepatitis C virus

ISG15: interferon stimulated gene 15

pegIFN/rib: pegylated interferon/ribavirin

MOI : multiplicity of infection

146

SUMMARY

Background & Aims: Up-regulation of interferon stimulated genes (ISGs), including interferon

stimulated gene 15 (ISG15) and other members of the ISG15 pathway, in pre-treatment liver

tissue of Hepatitis C virus (HCV) chronically-infected patients is associated with subsequent

treatment failure (pegylated interferon- /ribavirin, pegIFN/rib). Here, we study the effect of

ISG15 on HCV production in vitro. Methods: The levels of ISG15 and of its conjugation to

target proteins (ISGylation) were increased by plasmid transfection, or ISGylation was inhibited

by siRNA directed against the E1 activating enzyme Ube1L in Huh7.5 cells. Cells were infected

with HCV J6/JFH1 virus, and HCV RNA and viral titers determined. Results: Levels of both

HCV RNA and virus increased when levels of ISG15 and ISGylation were increased, and

decreased when ISGylation was inhibited. The effects of ISGylation on HCV are independent of

upstream IFN signaling: IFN -induced ISG expression is not altered by Ube1L knockdown. The

effect is also not likely secondary to a cytokine effect: treatment of cells with purified ISG15

does not inhibit HCV production. Conclusions: Although ISG15 has antiviral activity against

most viruses, ISG15 promotes HCV production. HCV might exploit ISG15 as a host immune

evasion mechanism, and this may in part explain how increased expression of ISGs, especially

ISG15, correlates with subsequent interferon-based treatment failure.

147

INTRODUCTION

HCV is adept at evading host antiviral mechanisms and is often resistant to the current standard

of care - combination treatment with PegIFN and Ribavirin. This regimen eradicates the virus in

only 50% of cases.

A number of mechanisms contribute to evasion and treatment resistance,

including cleavage of the RIG-I adaptor protein IPS1/MAVS/cardif by the HCV NS3/NS4A

protease and modulation of the host response by the HCV core protein (Li, et al., 2005; Loo, et

al. 2006). However, none of these mechanisms have been consistently demonstrated to play a

role in the clinical disease and thus cannot explain the ability of the virus to escape the host

response in patients.

Response to treatment can be predicted by levels of expression of interferon stimulated genes

(ISGs) in the liver prior to initiation of PegIFN/Rib treatment. Non-responders have increased

expression of a number of ISGs (Chen, et al.,2005; Feld, et al., 2007; Asselah, et al., 2008;

Asahina, et al., 2008; Sarasin-Filipowicz, et al., 2008) . Three of these ISGs are components of

the ISG15 ubiquitin-like pathway. ISG15 was the first ubiquitin-like protein to be described and,

like its homologue ubiquitin, is conjugated to proteins in a tightly regulated process called

ISGylation. The ISG15 E1-activating protein, Ube1L, coordinates with the E2-conjugating

enzyme (UbcH8) and the E3-ligase (CEB1) to join the C-terminus of ISG15 to a wide variety of

proteins ( Loeb, et al., 1992). ISG15 can be removed from its target proteins by USP18, an

ISG15 protease (Malakhov, et al. 2002). ISG15, CEB1, and USP18 are upregulated in the liver

tissue of patients infected with HCV who then did not respond to treatment with PegIFN/Rib

(Chen, et al. 2005). A functional association between this pathway and the regulation of HCV

production has been established: knockdown of USP18 increases the anti-HCV potency of

148

IFN Randall, et al., 2006) and ISG15 protein expression is highly upregulated in the

hepatocytes of treatment nonresponders, but is increased in the macrophages of treatment

responders. The ISG15 pathway is likely important to clinical HCV disease and to determining

treatment outcomes.

ISGylation is implicated in different cellular processes, but its role in viral biology is the one best

established (Dao, et al., 2005). ISG15 is targeted by a number of viruses in animal model and

cell culture systems. For example, non-structural protein 1B of Influenza B virus binds to the

free form of ISG15, preventing ISGylation (Yuan, et al., 2001).

Over-expression of ISG15 in

IFN- / -R knockout mice protects them from Sindbis virus-induced lethality and decreases

Sindbis virus replication in multiple organs (Lenschow, et al., 2005).

ISG15-/- mice are more

susceptible to influenza A and B, HSV1, Sindbis virus, and murine gammaherpesvirus68

infection; for Sindbis virus this effect is dependent on ISGylation (Lenschow, et al., 2007).

While these studies suggest a general role for ISG15 as an antiviral agent, a recent report found

that ISG15 can inhibit IFN responses after infection by NDV virus (Kim, et al., 2008a ).

ISGylation of the antiviral RIG-I enzyme inhibited IFN signaling in MEF cells (Kim, et al.,

2008b) . Thus, ISG15 inhibits viral production for many viruses, but it may promote production

for some.

In this study we examined the role of ISG15 and ISGylation in HCV production in vitro, using

the J6/JFH HCV infectious model. Unexpectedly, increasing the level of ISG15/ISGylation

promoted HCV production, and blocking ISGylation decreased both HCV RNA and viral titers.

This work therefore suggests a new context for the host ISG response to HCV: some aspects of

the host ISG response to HCV foster viral production, rather than inhibit it.

149

MATERIAL and METHODS

Cells and HCV FL-J6/JFH Virus

Huh7.5 cells and HCV FL-J6/JFH were kindly provided by Dr. Charles Rice (Rockefeller

University) (Lindenbach, et al., 2005). Cells were maintained in Dulbecco‘s modified Eagle

media (DMEM) supplemented with nonessential amino acids/10% fetal bovine serum/100 u/ml

ampicillin/100 μg/ml streptomycin at 37°C in a 5% CO2 humidified incubator.

ISG15 expression plasmid

Human full-length ISG15 was generated using pOTB7-ISG15 plasmid DNA (MGC clones, Open

Biosystems) as template, and the resulting PCR product was cloned into pcDNA4/HisMax

TOPO TA expression vector (Invitrogen). Primers used were: forward 5‘

ATGGGCTGGGACCTGACGGTG 3‘; reverse 5‘TTAGCTCCGCCCGCCAGGCTC 3‘.

Plasmid DNA for transfection studies was prepared using Plasmid Maxiprep Kit (Qiagen).

ISG15 Transfection and detection of ISG15 expression by western blot

8 g of ISG15 plasmid DNA or blank vector was transfected into Huh7.5 cells (2.5x105/ml, 5ml

per 6 cm culture dish) with Lipofectamine 2000 (Invitrogen) following kit protocol. 48 hours

later the transfected cells were harvested, washed (PBS), and lysed in 200ul lysis buffer (50mM

HEPES pH7.8, 500mM NaCl, 1% Triton x-100,1mM EDTA, protease inhibitor cocktail

(Sigma)). Proteins were separated by NuPAGE 4-12% Bis-Tris Gel and transferred onto

nitrocellulose membrane by Trans-Blot SD Semi-dry transfer cell (Bio-Rad). ISG15 expression

was assessed by using a polyclonal anti-ISG15 antiserum raised in rabbits against purified

human ISG15 protein – (Cedarlane). Blots were developed with OptiBlaze

WESTfemtoLUCENT kit (G Biosciences).

150

For studies of the effect of ISG15/ISGylation on HCV production, 0.3μg of ISG15 plasmid DNA

or blank vector was transfected into Huh7.5 cells in each well of the 96-well plate for 48 hours,

following which the cells were treated in the absence or presence of IFNα (0-1 U/ml) for 16

hours followed by infection with HCV FL-J6/JFH virus (MOI=0.3) for 6 hours rinsed, and

overlaid with fresh media. Two days post infection, cells were harvested for assessment of HCV

replication (RNA and infectious particles, see below).

Ube1L SiRNA knock down

Four Ube1L small inhibitory RNAs (siRNAs) were designed as follows (the sense strand

sequence is described): siUbe1L#1, 5′-CAUCUUUGCUAGUAAUCUA; siUbe1L#2, 5′-

CGAAUUGUGGGCCAGAUUA; siUbe1L#3, 5′- AUAGAGCGCUCCAAUCUCA; and

siUbe1L#4, 5′-GCAUGGAGUUUGCUUUCUG. The irrelevant siRNA (siIrr) is 5′-

GGCGCUUGUGGACAUUCUGTT. Chemically synthesized RNA oligos were prepared as

recommended by the manufacturer (Dharmacon, Lafayette, CO). One nanomole of RNA

duplexes was electroporated into 2.5 × 106 Huh-7.5 cells as previously described.

11 Thirty hours

after electroporation, cells were treated with IFN- (0–1 U/ml) for 15 hours, then either

harvested for determining siRNA knock down efficiency (Ube1L mRNA by real-time PCR) or

washed, infected with HCV FL-J6/JFH (MOI=0.3) for 6 hours, rinsed, and overlaid with fresh

media. 48 hours post infection, cells were harvested for assessment of HCV production (RNA

and infectious particles). Another similar experiment was performed using higher dosages of

IFN-α (0-1000 U/ml) to investigate whether silencing Ube1L and/or decreased ISGylation has

any effect on IFN down-stream ISG mRNA expression (real time PCR).

Quantification of Infectious HCV virion and HCV RNA


151

Viral titers were determined by limiting dilution analysis of culture supernatants as previously

described.17

For HCV RNA quantification, total cellular RNA was harvested and purified with

96-well RNA-easy columns (Qiagen, Mississauga, ON, Canada), reverse transcribed

(Superscript II, Invitrogen), cDNA was constructed (AnCT primer, Invitrogen), and real-time

PCR performed using Sybr Green mix and either the primers listed in Table 4-2-1

(Supplementary data) or HCV-specific primers 5-TGA GTG TCG TAC AGC CTC CA and 5′-

ACG CTA CTC GGC TAG CAG TC (Platinum Quantitative reverse-transcription PCR

Thermoscript One-step System, Invitrogen, Life Technologies) as described previously Randall,

et al., 2006).

Statistics:

Where appropriate, Student‘s t test was used to compare 2 categorical values and one-way

ANOVA was used to compare more than 2 categorical values. For western blot studies the

presented work was repeated at least three times.

152

RESULTS:

Increasing and decreasing ISGylation in Huh7.5 cells:

In order to test whether ISG15 conjugation plays a role in HCV replication/production, we

developed ways of increasing and decreasing ISG15 conjugation (ISGylation). Inducing

ISGylation can be difficult in certain cells: for example, in Hela cells ISGylation could only be

induced by overexpression of ISG15 in combination with its E1 activating enzyme Ube1L and its

E2 conjugating enzyme UbcH8 (Zhao, et al. 2005). However, in Huh7.5 cells over-expression

of ISG15 alone led to pronounced protein ISGylation in Huh7.5 cells (Figure 4-2-1a).

Combining over-expression of ISG15 with over-expression of Ube1L and/or UbcH8 did not

appreciably increase protein ISGylation beyond that observed with over-expression of ISG15

alone (data not shown). In order to inhibit ISGylation, the ISG15 E1 Ube1L enzyme was

knocked down with siRNA. Ube1L mRNA was successfully knocked down even in the presence

of increasing concentrations of IFNα (0-1 U/ml) (Figure 4-2-2). This maneuver abolished

ISGylation even in the presence of high levels of IFN (100 IU/ml) (Figure 4-2-1b). Thus, in

Huh7.5 cells ISGylation can be increased and decreased relatively easily.

153

Fig. 4-2-1. Modulation of ISGylation in Huh7.5 cells

Fig. 4-2-1. Modulation of ISGylation in Huh7.5 cells. (a) Western blot for ISG15 after transfection of Huh7.5 cells

with ISG15 or empty vector. U, Untreated; V, empty vector. The presence or absence of IFN- (100 U ml–1, 24 h) is

indicated. (b) Comparison of protein ISGylation (Western blot for ISG15) in the presence or absence of IFN-

(100 U ml–1, 24 h) following electroporation of irrelevant siRNA (siIrr) or siRNA specific to Ube1L (siUbe1L).

Molecular mass markers are shown on the left (kDa).

154

Fig. 4-2-2. Ube1L mRNA expression was silenced by siRNA

Fig. 4-2-2. Ube1L mRNA expression was silenced by siRNA. Huh7.5 cells were electroporated with irrelevant

siRNA (shaded bars) or siRNA specific to Ube1L (filled bars). Thirty hours after electroporation, cells were treated with increasing amounts of IFN- for 15 h before being harvested to determine the levels of Ube1L mRNA by real-

time PCR (normalized to β-actin). Data represent the means±SD of three replicates; the results shown are

representative of three similar experiments. *P<0.05 (versus irrelevant siRNA).

155

HCV RNA and virus are increased in parallel with ISGylation:

We next asked whether ISGylation (and ISG15) modulates HCV production. As seen in Figure

4-2-3, increasing ISGylation by over-expression of ISG15 significantly increased the production

of both HCV RNA and virus, even in the presence of increasing IFNα, suggesting that increased

ISG15/Isgylation blunts IFNα anti-HCV activity (Figure 4-2-4) in J6/JFH HCV in vitro culture

system. By contrast, silencing of the ISG15 Ube1L E1 enzyme decreased levels of HCV RNA

and virus both at baseline (in the absence of IFN ) and in the presence of IFN , an effect that

was more pronounced for HCV viral titers (Figure 4-2-5). To ensure that the siRNA were

selective, we tested the effects of four individual Ube1L siRNAs and compared these to the

effects of pooled Ube1L siRNA. As shown in Figure 4-2-6, all four individual siRNAs had a

similar inhibitory effect on HCV viral particle secretion when compared to the pooled siRNA.

These data argue that the effect we observed is specific to the knockdown of Ube1L. Taken

together, these data suggest that ISGylation is important for baseline HCV production.

156

Figure 4-2-3. ISG15 promotes HCV production in vitro

Fig. 4-2-3. ISG15 promotes HCV production in vitro. Huh7.5 cells were transfected with empty vector (V) or ISG15

plasmid DNA for 48 h before the cells were infected with FL-J6/JFH as described in Methods. HCV RNA was quantified by real-time PCR (a), and HCV virus particle titres were assessed by serial dilution of culture

supernatants (b). Data represent the means±SD of three replicates; the results shown are representative of three

similar experiments. U, Untreated control. ***P<0.001; *P<0.05 (versus empty vector).

157

Figure 4-2-4. Increased ISG15/ISGylation decreases IFNα anti-HCV activity

Fig. 4-2-4. Increased ISG15/ISGylation decreases IFN- anti-HCV activity. Huh7.5 cells were transfected with

empty vector (shaded bars) or ISG15 plasmid DNA (filled bars) for 48 h before the cells were treated with

increasing amounts of IFN- , as indicated, for 16 h followed by infection with FL-J6/JFH as described in Methods.

HCV RNA was quantified by real-time PCR (a), and HCV virus particle titres were assessed by serial dilution of

culture supernatants (b). Data represent the means±SD of three replicates; the results shown are representative of

three similar experiments. *P<0.05 (versus empty vector).

158

Figure 4-2-5. Silencing Ube1L inhibits HCV production.

Fig. 4-2-5. Silencing Ube1L inhibits HCV production. Huh7.5 cells were electroporated with irrelevant siRNA

(shaded bars) or Ube1L siRNA (filled bars) and the cells were treated with different concentrations of IFN- as

indicated. HCV RNA (a) and the number of infectious particles (b) were determined as described in Methods. Data

represent the means±SD of three replicates; the results shown are representative of three similar experiments.

*P<0.05 (versus siIrr-transfected cells).

159

Figure 4-2-6. Comparison of individual vs pooled Ube1L siRNA

Fig. 4-2-6. Comparison of individual versus pooled Ube1L siRNAs. siRNA was electroporated into Huh7.5 cells, after which the cells were infected with FL-J6/JFH as described in Methods. Viral particles were titrated 48 h after

infection. Data represent the means±SD of three replicates; the results shown are representative of three similar

experiments.

160

Silencing Ube1L does not affect upstream IFN signaling

Increased ISGylation can prolong STAT1 phosphorylation, suggesting that ISGylation might

play an important role in IFN signaling (Malakhova, et al. 2003) . To test this hypothesis in the

HCV model, we assessed the effect of decreasing ISGylation by Ube1L knockdown on

downstream ISG expression in the presence of IFNα. As shown in Figure 4-2-7, the expression

of a number of ISG transcripts was not affected following Ube1L SiRNA knock down in the

presence of IFNα.

161

Figure 4-2-7. IFN -induced ISG expression following Ube1L SiRNA knockdown

Fig. 4-2-7. IFN- -induced ISG expression following Ube1L siRNA knockdown. Huh7.5 cells were electroporated

with irrelevant (shaded bars) or Ube1L (filled bars) siRNA, after which the cells were exposed to different

concentrations of IFN- as indicated. Cells were lysed 24 h after the introduction of IFN- , and ISG expression

was quantified using real-time PCR normalized to β-actin expression levels. Data represent the means±SD of three replicates; the results shown are representative of three similar experiments.

162

DISCUSSION:

ISG15 is one of the most abundant ISGs induced after virus infection and type I IFN treatment,

and we and others have found that increased pretreatment ISG15 expression in the livers of

HCV-infected patients predicts subsequent treatment failure (Chen, et al.,2005; Feld, et al.,

2007; Asahina, et al., 2008; Asselah, et al., 2008; Sarasin-Filipowicz, et al., 2008) . Although

ISG15 is generally considered antiviral, we now present evidence that ISGylation promotes HCV

production, blunts the anti-HCV effect of IFN , and is particularly relevant for steps downstream

of HCV RNA replication. Thus, aspects of the host ISG response favour, rather than inhibit,

HCV persistence. ISG15 is a novel mechanism for viral persistence, and ISGylation is a possible

target for therapy of HCV infection.

As noted in the Introduction, the effect of ISG15 on viral production may be specific to the virus.

For example, ISG15 can be antiviral to Sindbis, influenza, HSV, HIV, and Ebola virus

(Lenschow, et al., 2005; Okumura, et al., 2006; Lenschow, et al., 2007; Zhang, et al., 2007),

but ISGylation does not contribute to murine susceptibility to LCMV and VSV (Knobeloch, et al.

2005) , nor to Hepatitis B virus replication in ISGylation-deficient mice (Ube1L -/-) (Kim, et al.,

2008) . Although ISG15 may also promote viral production, by acting as a negative regulator of

the innate immune response through its conjugating to RIG-I (Kim, et al., 2008) , this

mechanism is unlikely to contribute to our observed effects as Huh7.5 cells are deficient in RIG-I

(Sumpter, et al., 2005). Our data suggest that HCV exploits the ISG15/ISGylation pathway to

increase HCV production: over-expression of ISG15, which increases ISGylation in Huh7.5 cells

163

(Figure 4-2-1), increased HCV RNA 3-fold and viral titers 2.2-fold (Figure 4-2-3). Blocking

ISGylation by knockdown of Ube1L decreased HCV RNA but largely abolished production of

infectious virus (Figure 4-2-5).

Another approach to examining the role of ISG15 in HCV production would be to decrease

ISG15 mRNA using specific siRNA. We have not employed this method in the current study –

in preliminary work we found that ISG15-specific siRNA was not able to consistently decrease

ISG15 mRNA in Huh7/7.5 cells, particularly in the presence of IFN (data not shown). However,

others have demonstrated that knockdown of ISG15 in two HCV replicon models (Con1 and

murine MH1 cells) resulted in decreased HCV production both with and without IFN Broering,

et al., 2008).

Although this study does not directly address the role of ISGylation, it adds

evidence for the permissive role of the ISG15 pathway in the HCV lifecycle.

Our data provide mechanistic insight into how ISG15 affects HCV production. ISG15 exists in

three forms: 1) a free, unconjugated intracellular protein, 2) conjugated to viral and/or host target

proteins, and 3) an extracellular cytokine(Recht, et al., 1991; D‘Cunha, et al., 1996a and 1996b;

Lai, et al., 2009;). All three forms could potentially affect HCV viral production. In other

systems, the free form of ISG15 has been shown to inhibit the release of Ebola virus-like

particles by interfering with the activity of Nedd4 (Malakhova, et al. 2008; Okumura, et al.,

2008) . ISGylation is critical to the effect of ISG15 on Sindbis virus, and ISGylation is targeted

by the human influenza NS1 protein (Yuan, et al., 2001; Lenschow, et al., 2007) .

As a

cytokine, purified ISG15 can activate NK and cytotoxic T-cells, stimulate IFN- production, and

164

induce dendritic cell maturation and neutrophil recruitment (Recht, et al., 1991) . Our data argue

that ISGylation is the predominant mechanism through which ISG15 affects HCV production.

Blocking ISGylation by Ube1L knockdown does not decrease free ISG15 but dramatically

reduces HCV viral titers and significantly reduces HCV RNA. In order to test for a direct

cytokine role of ISG15 we exposed Huh7.5 cells to high dose purified ISG15 (2μg/ml) for 36

hours before cells were infected with J6/JFH virus (MOI=0.3) as before. Although the dose we

used is considerably higher than that used by D‘Cunha et al (100 ng/ml) to define the cytokine

effect of ISG15( D‘Cunha et al., 1996b), we were unable to find any inhibition of HCV

production, nor did we find any reduction in the ability of IFN to stimulate ISG expression

(data not shown).

The current study demonstrates that increasing ISGylation promotes HCV production and blunts

that anti-HCV effect of IFNα. However, previous work from our group demonstrated that

decreasing expression of USP18, the ISG15 protease, increases ISGylation yet potentiates IFN

anti-HCV activity (Randall, et al., 2006) . These data at first blush are conflicting, but only if

one assumes that USP18 and ISG15 work entirely through the same pathway. In fact, USP18

clearly has additional targets beyond ISG15, and manipulating USP18 expression has effects on

protein expression that are independent of ISG15. For example, EGF receptor synthesis is

regulated by USP18 ( Duex, et al., 2009). USP18 has both protease-dependent and –independent

functions (Malakhova, et al., 2006). Our preliminary data would support that USP18 has a role in

HCV production that is independent of ISG15 and of ISG15 protease activity (Chen, et al., 2008).

Taken together, the data from our group suggest that ISGylation is necessary but not sufficient

for HCV production.

165

Blocking ISGylation enhances the anti-HCV effect of IFN (Figure 4-2-5). This effect is not

likely to be mediated at the level of IFN signaling, since Ube1L knockdown did not promote (or

inhibit) IFN-dependent ISG expression, which is the readout of activation of the IFN pathway

(Figure 4-2-7). Although ISGylation may play an important role in the regulation of the Jak-

STAT pathway and IFN signaling in some cells (Malakhov, et al., 2002a and 2002b; Ritchie, et

al.,2002), the IFN signaling pathway is intact in ISG15-/- and Ube1L-/- mice (Osiak, et al., 2005;

Kim, et al., 2006) . In our work, IFN signaling appears to be unaffected despite knockdown of

Ube1L, and Ube1L knockdown inhibits HCV production even in the absence of IFN . This data

suggests that ISGylation of viral (or host) proteins is directly important for the viral life cycle.

The conjugation of ISG15 to cellular or viral proteins might alter their function, or compete for

ubiquitination. For example, in order for HIV virus to be secreted from infected cells the Gag

protein must be ubiquitinated and then recruited to the endosomal transport complex. ISG15

conjugates to Gag, prevents its ubiquitination, and thus inhibits HIV release (Okumura, et al.,

2006). In another example, ISG15 conjugation to interferon regulatory factor 3 (IRF3), a key

signal-transducing factor for IFN-dependent immune responses, protects IRF3 from Ub-

mediated degradation (Lu, et al., 2006). Thus, ISGylation of HCV proteins or of host proteins

important for the HCV lifecycle may alter their function or protect them from degradation – the

specific steps involved in the ISG15 effect remain to be defined.

In summary, our in vitro data strongly argue that ISG15, and specifically ISGylation, is

important to the HCV life cycle in an infectious cell culture model of HCV. This study offers one

explanation for how increased baseline expression of some ISGs, including ISG15, correlates

166

with treatment failure in HCV infected patients. Targeting ISGylation – or specific targets of

ISGylation - may identify new anti-viral therapies for HCV.

167

Chen, et al. ISG15 promotes HCV production in vitro

Supplementary data

Table 4-2-1. Real-time RT-PCR primers

Gene name Annotation Forward primer Reverese primer Amplicon length (bp)

USP18 Ubiquitin specific protease 18 CAGACCCTGACAATCCACCT AGCTCATACTGCCCTCCAGA 164

ISG15 interferon, alpha-inducible protein 1 CGCAGATCACCCAGAAGATT GCCCTTGTTATTCCTCACCA 185

IFI-6-16 CTCGCTGATGAGCTGGTCT ATACTTGTGGGTGGCGTAGC 148

IFIT1 GCAGCCAAGTTTTACCGAAG GCCCTATCTGGTGATGCAGT 109

MxA myxovirus (influenza virus) resistance 1 GTGCATTGCAGAAGGTCAGA CTGGTGATAGGCCATCAGGT 140

Viperin CTTTTGCTGGGAAGCTCTTG CAGCTGCTGCTTTCTCCTCT 131

168

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Lipopolysaccharide activates the expression of ISG15-specific protease UBP43 via

interferon regulatory factor 3. J Biol Chem 277, 14703–11.

25. Malakhova, O.A., Kim, K.I., Luo, J.K., Zou, W., Kumar, K.G., Fuchs, S.Y., Shuai,

K., Zhang, D.E. (2006). UBP43 is a novel regulator of interferon signaling

independent of its ISG15 isopeptidase activity. EMBO J 25, 2358-67.

26. Malakhova, O. A., Zhang, D. E. (2008). ISG15 inhibits Nedd4 ubiquitin E3 activity

and enhances the innate antiviral response. J Biol Chem 283, 8783–8787.

27. Malakhova, O.A., Yan, M., Malakhov, M.P., Yuan, Y., Ritchie, K.J., Kim, K.I.,

Peterson, L.F., Shuai, K., Zhang, D.E. (2003). Protein ISGylation modulates the

JAK-STAT signaling pathway. Genes Dev. 17, 455-60.

28. Okumura, A., Pitha, P.M., Harty, R.N. (2008). ISG15 inhibits Ebola VP40 VLP

budding in an L-domain-dependent manner by blocking Nedd4 ligase activity. Proc

Natl Acad Sci USA 105, 3974–3979.

29. Okumura, A., Lu, G., Pitha-Rowe, I., and Pitha, P.M. (2006). Innate antiviral response targets HIV-1 release by the induction of ubiquitin-like protein ISG15.

171

Proc Natl Acad Sci USA 103, 1440–1445.

30. Osiak, A., Utermöhlen, O., Niendorf, S., Horak, I., Knobeloch, K.P. (2005). ISG15,

an I nterferon-stimulated ubiquitin-like protein, is not essential for STAT1 signaling

and responses against vesicular stomatitis and lymphocytic choriomeningitis virus.

Mol Cell Biol 25, 6338-45.

31. Randall, G., Chen, L., Panis, M., Fischer, A.K., Lindenbach, B.D., Sun, J.,

Heathcote, J., Rice, C.M., Edwards, A.M. & other authors. (2006). Silencing of

USP18 potentiates the antiviral activity of interferon against hepatitis C virus infection.

Gastroenterology 131, 1584-91.

32. Recht, M., Borden, E.C., Knight, E. (1991). A human 15-kDa IFNinduced protein

induces the secretion of IFN-gamma. J Immunol 147, 2617-2623.

33. Ritchie, K.J., Malakhov, M.P., Hetherington, C.J., Zhou, L., Little, M.T.,

Malakhova, O.A., Sipe, J.C., Orkin, S.H., Zhang, D.E. (2002). Dysregulation of

protein modification by ISG15 results in brain cell injury. Genes Dev. 16, 2207-12.

34. Sarasin-Filipowicz, M., Oakeley, E.J., Duong, F.H., Christen, V., Terracciano, L.,

Filipowicz, W.,Heim, M.H. (2008). Interferon signaling and treatment outcome in

chronic hepatitis C. Proc Natl Acad Sci USA 105, 7034-7039.

35. Sumpter, R., Jr., Y. M. Loo, E. Foy, K. Li, M. Yoneyama, T. Fujita, S. M. Lemon,

and M. Gale, Jr. (2005). Regulating intracellular antiviral defense and permissiveness

to hepatitis C virus RNA replication through a cellular RNA helicase, RIG-I. J. Virol.

79:2689-2699

36. Yuan, W., Krug, R.M. (2001). Influenza B virus NS1 protein inhibits conjugation of

the interferon-induced ubiquitin-like ISG15 protein. EMBO J 20, 362–371.

37. Zhang, Y., Burke, C.W., Ryman, K.D., and Klimstra, W.B. (2007). Identification

and characterization of interferon-induced proteins that inhibit alphavirus replication. J

Virol 81, 11246–11255.

38. Zhao, C., Denison, C., Huibregtse, J.M., Gygi, S., Krug, R.M. (2005). Human

ISG15 conjugation targets both IFN-induced and constitutively expressed proteins

functioning in diverse cellular pathways. Proc Natl Acad Sci USA 102, 10200-5.

172

Chapter 5: General Discussion

With over 170 million people chronically infected, HCV infection is a major health burden.1,2

The majority of the infected patients cannot clear the virus, leading to chronic infection and often

progressing to liver cirrhosis and hapatocelluar carcinoma. Current treatment with the

combination therapy of pegylated IFNα/Rib clears the virus from at most 50% of all the patients

and this therapy is associated with many side-effects.1 One goal of my thesis was to predict in

advance which patients would benefit from therapy and which could be spared the treatment.

Predicting who will respond to treatment before initiation of therapy would benefit patients in

several ways. First, it would certainly boost the willingness of the patients to go through the full

course of treatment if they are predicted to be responders; compliance is a serious issue. Second,

it would avoid causing patients unnecessary side-effects and save the cost of treating those who

are predicted to be non-responders. There is no diagnostic tool available in the clinic to predict

treatment response in HCV infected patients before treatment starts. Therefore, developing a

prognostic tool and studying the molecular mechanism of IFN resistance in those patients who

do not repond to IFN therapy are of huge interest in HCV research.

5.1 Identification of an HCV response signature and its validation

Virus-host interactions play an important role in determining response status. Given the fact that

different individuals infected with the same genotype of HCV have different outcomes, this

indicates that differences in host factors might be involved. My PhD research focused on host

factors that determine response status in HCV infected patients.

173

There are several approaches to study host response to a virus infection. Traditionally, effort

would be focused on a few candidate genes that had been identified to be associated with virus

infections previously, especially in the immune response, viral replication and apoptosis

pathways. While this approach could certainly validate some previous findings, it is not likely to

reveal any new gene /pathway that is involved in host response to a given viral infection. I used

genome-wide approaches to screen for genes differentially expressed between virally infected

and normal control tissues or between treatment responders (R) and non-responders (NR) in the

HCV infected livers. Expression levels of more than ten thousands human genes were studied on

the same microarray chip simultaneously. This approach, unbiased by any predetermined

hypotheses, should reveal the true differences at the mRNA level between groups of samples

studied and shed light on new genes/pathways that are likely involved in any particular disease

state.

In order to develop a prognostic tool that could be used to predict treatment response to IFN

combination therapy in HCV infected patients, I screened for hepatic gene expression differences

between treatment responders(R) and non-responders(NR) using a cDNA microarray comprising

19,000 human genes. 31 HCV chronically infected patients whose liver biopsies were obtained

prior to the onset of treatment were divided into responders (R, n=16) and non-responders (NR,

n=15) based on their after-treatment status. I found a few hundred genes whose expression

levels were different in the HCV infected livers compared to normal liver controls, and 18 genes

whose levels of expression were consistently and statistically different between R and NR ( page

55, Figure 2-1). Based only on the relative expression levels of these 18 genes, 30 out of 31

patients could be classified correctly into their treatment outcome group ( page 56, Figure 2-2).

174

Because this response signature was generated in a retrospective study, I also initiated a

prospective validation study by recruiting another 47 HCV chronically infected patients. Based

on the expression levels of the 18 signature genes, the positive prediction value (PPV) reached

96%, as derived using 4 different predicting methods (Page 81, table 3-2). This indicates that this

HCV response signature can be used to predict treatment response prospectively.

Although microarray technology is an important tool for biological research, it has not yet been

employed in a clinical diagnostic setting.3 The technology has the reputation of being noisy:

studies addressing the reproducibility and reliability of microarray data across different

laboratories and platforms have often been inconsistent.4-13

There is general agreement that the

variability inherent to DNA microarray technology is due to the variability in the biology, in the

technology platforms and in the statistical treatment of the data.

There are a number of microarray platforms independently developed by industry and academia.

Two major types of platforms have been developed: one is based on oligo nucleotides (50-mer or

70-mer)4,5,9

and the other is based on cDNA fragments (about 200 nucleotides) 6 .

Different

protocols are used by different laboratories for RNA preparation and labeling. Some labs use

total RNA without amplification15

while others amplify the mRNA before reverse-transcription

to integrate different labels into the generated cDNA (Probes).13

Different statistical and

computational tools are used in the analysis of the microarray results. Indeed, a study by Irizarry

et al. on microarray data reproducibility has demonstrated that disagreement observed in some of

the studies may also be due to questionable statistical analysis. 14

Biological variability is

another factor contributing to the already complex inconsistency in microarray experiments.

175

Due to these differences it has proven challenging to extract reproducible, biologically

meaningful information from different DNA microarray experiments that address the same, or

very similar biological questions. In this regard, it is necessary to verify the gene list generated

by high-throughput microarray studies using an orthogonal method, such as real-time PCR. As

shown in Figure 2-1 (page 55), the differences of the mRNA expression levels of all these 18

genes have been successfully confirmed by real-time PCR.

Since its publication on Gastroenterology, this HCV response signature has gained wide

acceptance in the HCV field. It has been validated prospectively by our group15

and confirmed

by many other independent labs as well.16,17

One of the major characteristics of this response

signature is the elevated expression of pretreatment hepatic ISGs in treatment non-responders

(NRs). Feld et al.16

used a microarray-based approach to shed light on the mechanisms of action

of combination PegIFNα/RBV therapy and they confirmed our previous observation that ISGs

are more highly expressed in the pre-treatment liver tissue of non-responders than responders.

Asselah et al. recently identified a two-gene signature (IF127 and CXCL9) that can accurately

predict treatment response in 79% of patients being treated for their hepatitis C.17

Although the

gene identities are different from ours, they all belong to ISGs. Taking all these results together,

preactivation of the IFN pathway or other pathways that lead to increased expression of

downstream ISGs correlates with NR.

5.2 Cell-type specific expression of ISGs underlies treatment response in HCV infected

patients

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My previous expression experiments using microarrays and real-time PCR were based on total

RNA isolated from HCV infected liver, which contains mixtures of RNA from different cell

types: hepatocytes, infiltrating lymphocytes, macrophages, and other cell types (Kupffer cells,

stellate cells…). Because only 0.5-10% of hepatocytes are infected with virus in chronic HCV

infected liver, it is very important to learn where the response signature comes from, in order to

get at the underlying mechanism. The simplest hypothesis to explain the HCV response signature

based on differential expression was to assume that the signatures all derived from hepatocytes,

due to the abundance of this cell type in the liver. To test this hypothesis, I looked for cell-type

specific expression using different antibodies in immunohistochemistry against the proteins

encoded by the various dysregulated genes (ISG15, MxA, etc). ISG15 and MxA were selected as

representative proteins because they are ISGs and the majority of the dysregulated genes in the

18-gene signature set were ISGs. ISG15 was also one of the major players in the ubuquitin-like

signaling pathway that I identified was involved in IFN resistance. MxA protein expression had

been seen before to be differentially expressed in liver tissues infected with HCV, which served

as a control for my studies.

In collaboration with Dr. Maha Guindi at UHN, I used liver biopsies from patients chronically

infected with HCV to see if hepatocytes or infiltrating lymphocytes are the major source of ISG-

producing cells and to see if there exist distinct expression patterns in ISG-producing cells

between treatment responders and non-responders.

Contrary to my previous hypothesis that the HCV response signature was all derived from

hepatocytes, responders and non-responders have distinct expression patterns of ISG-producing

177

cells: hepatocyte staining was predominantly found in non-responders and macrophages were

stained preferentially in responders. (Figure 3-4A and 4B, page 92-93)

These IHC data suggest that the HCV response signature we identified using cDNA microarrays

was derived from different cell types in the HCV infected livers of treatment responders (R) and

non-responders (NR). Considering that the majority of cells in the human livers are hepatocytes,

restricted expression of ISG15 and MxA in hepatocytes of NR livers may help explain why there

is increased expression of ISGs in the pretreatment NR livers. Similarly, the many fewer

macrophages present in the liver also explain lower expression of ISGs in the R livers.

Therefore, the differential baseline ISG expression between Rs and NRs revealed using cDNA

microarrays is not due to the difference in absolute expression levels, but to the different cell

types with different abundance in the liver. This intriguing finding suggests in general that cell-

type specific gene expression patterns are under-explored parameters in tissue microarray studies.

Our findings of distinct expression pattern of ISG-producing cells in R and NR have at least

three implications:

First, different ISG-expressing cell types in the liver of R and NR may allude to some novel

mechanisms of HCV pathology and IFN resistance. For example, because HCV replicates

primarily in hepatocytes, increased ISG expression in the hepatocytes of NR livers may stimulate

HCV replication and virion production in the hepatocytes, because both ISG15 and USP18

stimulate HCV production in cell culture. As a result, more HCV may already be present in the

livers of NRs before initiation of IFN treatment. Increased HCV within the hepatocytes before

178

treatment coupled with the blunting effect of USP18 and ISG15 on IFN anti-HCV activity

following treatment may culminate in rendering the patients nonresponsive to IFN. On the

contrary, increased ISG15 and MxA expression in the macrophages from R livers might have

little effect on HCV replication because most HCV are present in hepatocytes, not in

macrophages in the liver, of patients with chronic HCV infections.

Second, ISG15 and MxA expression in macrophages from R livers indicate that HCV may

primarily activate these immune-related cells in Rs. Consequently, a stronger immune response

in R might be induced to facilitate virus clearance. Consistent with this hypothesis, I found the

expression levels of some immune-related and inflammation-involved genes are increased in the

livers of Rs compared to NRs (figure 3-6, Page 107). Taking all these data together, the logical

hypothesis for treatment nonresponse is either because the HCV infected hepatocytes developed

mechanisms to counteract the action of IFN, or because there is a defect in macrophage

activation in NR livers or both.

Third, the distinct expression patterns of ISG-producing cells in R and NR detected by IHC may

be used to predict whether a given patient will or will not respond to treatment, without checking

the expression levels of the 18 signature genes. This IHC procedure can be integrated easily into

the routine assessment of liver biopsies for the degree of fibrosis and inflammation in HCV

infected livers.

5.3 ISGs with pro-HCV roles

The innate immune response is the first line of defence against viral infections. It is initiated by

the pattern recognition receptor (PRR), which senses viral genomes through the pathogen

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associated molecular patterns (PAMPs), such as double-stranded RNA (dsRNA), single-stranded

RNA (ssRNA) and unmethylated CpG motifs in DNA, and lead to the production of type I IFNs

(IFNα and IFNβ).18-20

Binding to the surface receptor IFNAR1/2 heterodimer, type I IFNs

induce the expression of a few hundred interferon stimulate genes (ISGs) through activation of

the Jak/STAT signaling pathway.21

Although the functions of most of these ISGs are not known,

some of them, such as PKR, MxA, and OAS2, etc, have anti-viral activity. 22

While previous studies suggest a general role for ISG15 as an antiviral agent, a recent report

found that ISG15 can inhibit IFN responses after infection by NDV virus .23

ISGylation of the

antiviral RIG-I enzyme inhibited IFN signaling in MEF cells. 23

Thus, ISG15 inhibits viral

production for many viruses, but it may promote production for some.

In order to understand the role of ISG15 in HCV infection and viral resistance to IFN therapy, I

examined the role of ISG15 and ISGylation in HCV production in vitro, using the J6/JFH HCV

infectious model. Unexpectedly, increasing the level of ISG15/ISGylation promoted HCV

production, and blocking ISGylation decreased both HCV RNA and viral titers. This work

therefore suggests a new context for the host ISG response to HCV: some aspects of the host ISG

response to HCV foster viral production, rather than inhibit it. The exact mechanism underlying

this is not clear, but at least for ISG15, this does not result from perturbation of the type I IFN

signaling pathway because modulation of ISG15 conjugation has little effect on down-stream

ISG expression, a readout of type I IFN signaling pathway, as shown by real-time PCR (Figure

4-2-7, page 161).

180

USP18 also promotes HCV production in a cell culture model. Over-expression of both protease

-active and -inactive mutant USP18 enhanced HCV RNA replication within cells and viral

particle secretion into culture medium (manuscript in preparation). This also indicates that

USP18 promotes HCV production independent of its ISG15 protease-specific activity.

Considering the fact that increased expression of ISG15, USP18 and a few other ISGs in the

pretreatment liver tissues is correlated with treatment non-response (my microarray study), this

enhanced ISG expression may play at least two roles within hepatocytes: 1) promoting HCV

replication and secretion; and 2) blunting subsequent IFN anti-HCV activity. These two aspects

may culminate in making patients not respond to the IFN based treatment.

5.4 USP18 and its role in HCV infection and viral resistance to IFN therapy

The specific identities of the ISGs identified in our microarray study suggest a possible

mechanism for treatment nonresponse. Three of the genes that are overexpressed in non-

responders, interferon stimulated gene 15 (ISG15), ubiquitin specific protease 18

(USP18/UBP43), and CEB1/Herc5 (a HECT domain ISG15 E3 ligase), are linked to a newly

defined ubiquitin-like protein (Ubl)/ ubiquitin specific protease (ISG15/USP18) pathway.

The ISG15/USP18 pathway is involved in post-translational modification of some proteins, and

is related to the ubiquitination pathway. ISG15 is a ubiquitin-like protein that, like ubiquitin,

conjugates to its cellular targets through a series of enzymatic steps. Conjugation involves first

an E1 activating enzyme (Ube1L),24

then an E2 conjugating enzyme (UbcH8, UbcH6) 25,26

, and

181

finally an E3 ligase (EFP, CEB1/Herc5) 27,28

. The C-terminal LRLRGG sequence of ISG15 is

required for conjugation to the lysine residues of target proteins. ISG15 can be stripped from its

target proteins by the USP18 isopeptidase. 29

The ISG15 conjugation process is reversible and controlled by USP18 (UBP43), an IFN-

inducible cysteine protease of the ubiquitin-specific protease (USP) family.29

USP18 appears to

counteract the effects of interferon; lack of USP18 results in enhanced and prolonged STAT1

phosphorylation, DNA binding, and increased induction of hundreds of ISGs.30

By contrast,

USP18 knock out mice show greater resistance to the cytopathic effects of a number of viruses

including lymphocytic choriomeningitis virus (LCMV), vesicular stomatitis virus (VSV), and

Sindbis virus (SNV).31

USP18-deficient cells exhibit high levels of ISG15 modified proteins

(ISGylation). Furthermore, they are hypersensitive to type I IFN and undergo apoptosis upon

IFN stimulation. 32

Thus, USP18 appears to be a negative regulator of IFN signaling.

Although ISG15 may play a role in the anti-HCV response, the ability of USP18 to regulate the

anti-HCV interferon response may be independent of its ability to deconjugate ISG15. Ablation

of ISG15 33

or its E1 activating enzyme Ube1L in mice 34

did not reverse the phenotype of the

USP18 knockout, nor affect IFN-induced JAK/STAT signaling, indicating that neither ISG15

nor ISGylation is essential in JAK/STAT signaling. It was recently reported that USP18

negatively regulates JAK–STAT signaling independently of its isopeptidase activity.35

In this

study, USP18 action was specific to type I IFN responses and achieved through a direct

interaction between USP18 and the IFNAR2 subunit of the type 1 IFN receptor. Binding of

exogenous and endogenous USP18 to IFNAR2 in vivo interfered with the JAK-receptor

182

interaction and led to inhibition of the downstream phosphorylation cascade and other signaling

events. Whether this is a cell- or species-specific mechanism remains to be determined.

Data from my study clearly show that USP18 modulates IFN anti-HCV activity. Increased

pretreatment hepatic expression of USP18 mRNA may inhibit IFN activity against HCV. Indeed,

silencing USP18 by specific siRNA potentiated IFN anti-HCV activity in cell culture (figure 4-1-

2, page 126), and this effect may be mediated through the enhanced and prolonged activation of

the Jak/STAT signaling pathway (figure 4-1-7, page 136), leading to the increased expression of

down-stream anti-viral ISGs (figure 4-1-6, page 134). Additionally, over-expression of the

USP18 gene in Huh7.5 cells promoted HCV replication/production and blunted IFN anti-HCV

activity (manuscript in preparation). Taking these observations together, increased expression of

USP18 in the pretreatment NR livers may play two roles that cause the patients to not respond to

treatment: 1) it may stimulate HCV production within hepatocytes and, as a result, increase the

pre-treatment viral load in livers of subsequent NR; 2) it may blunt IFN anti-HCV activity

following IFN treatment.

In order to understand the mechanism by which increased USP18 stimulates HCV production, I

over-expressed USP18 in Huh7 cells harboring the HCV replicon, in collaboration with Dr.

Chris Richardson (Dalhousie University). We found that USP18 did not affect HCV RNA

replication, suggesting that the effect of USP18 on HCV production may be mediatd by a step

prior to replication, such as viral entry. Interestingly, although I was unable to investigate the

effect of USP18 on the expression of the HCV receptor, USP18 can modulate the expression of

other cell surface proteins. Knocking-down Usp18 in several cell lines reduced expression levels

183

of EGFR by 50-80% while overexpression of Usp18 elevated EGFR levels in a manner requiring

the catalytic cysteine of Usp18.36

In their study, analysis of metabolically radiolabeled cells

showed that the rate of EGFR protein synthesis was reduced up to 4 fold in the absence of Usp18.

Interestingly, this dramatic reduction occurred despite no change in the levels of EGFR mRNA.

This suggests that depletion of Usp18 inhibited EGFR mRNA translation. Whether USP18

regulates the expression of HCV entry receptors remains to be determined.

184

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30. Malakhova OA , Yan M , Malakhov MP , Yuan YZ , Ritchie KJ , Kim KI , Peterson LF ,

Shuai K , Zhang DE . Protein ISGylation modulates the JAK–STAT signaling pathway. Genes

Dev 2003;17: 455–460

31. Ritchie, K. J., C. S. Hahn, K. I. Kim, M. Yan, D. Rosario, L. Li, J. C. de la Torre, and D. E.

Zhang. Role of ISG15 protease UBP43 (USP18) in innate immunity to viral infection. Nat.

Immunol. 2004;10:1374-1378.

32. Ritchie KJ , Malakhov MP , Hetherington CJ , Zhou L , Little MT , Malakhova OA , Sipe JC ,

Orkin SH , Zhang DE. Dysregulation of protein modification by ISG15 results in brain cell

injury. Genes Dev 2002;16: 2207–2212

33.Osiak, A., O. Utermöhlen, S. Niendorf, I. Horak, and K.-P. Knobeloch. ISG15, an interferon-

stimulated ubiquitin-like protein, is not essential for STAT1 signaling and responses against

vesicular stomatitis and lymphocytic choriomeningits virus. Mol. Cell. Biol. 2005;25:6338-6345.

34.Kim KI, Yan M, Malakhova O, Luo JK, Shen MF, Zou W, de la Torre JC, Zhang DE. Ube1L

and protein ISGylation are not essential for alpha/beta interferon signaling. Mol Cell Biol.

2006 ;26(2):472-9.

35. Malakhova OA, Kim KI, Luo JK, Zou W, Kumar KG, Fuchs SY, Shuai K, Zhang DE.

UBP43 is a novel regulator of interferon signaling independent of its ISG15 isopeptidase activity.

EMBO J. 2006 ;25(11):2358-67

36. Duex JE, Sorkin A.RNAinterference screen identifies Usp18 as a regulator of epidermal

growth factor receptor synthesis. Mol Biol Cell. 2009 ;20(6):1833-44.

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Chapter 6: Future work

Future effort should be directed at better understanding how the ISG15/USP18 signaling

pathway modulates IFN anti-HCV activity and its role in virus resistance. It will also be

important to explore the basis for cell-type specific gene expression patterns in Rs and NRs.

With the new findings of a role for an IL28B SNP on viral clearance (both treatment induced and

spontaneous clearance) in HCV infected patients by GWAS, HCV research should be focused on

the dysregulated type III IFN pathway and preactivation of IFN signaling in viral resistance.

6.1. Identification of ISG15 and USP18 interacting proteins

Over-expression of ISG15 promoted HCV RNA replication and equally boosted virus particle

production, while inhibition of ISG15 conjugation (ISGylation) by silencing its E1 activating

enzyme Ube1L caused a more pronounced decrease in viral particle formation than HCV RNA

replication.1

This indicates that ISGylation is important for the HCV life cycle and that ISG15

targets are involved after the replication step. In order to understand the mechanism by which

ISG15 modulates IFN anti-HCV activity, it is essential to find out which host or viral proteins

are involved in this process. To this end, I generated ISG15 constructs with triple-tags to identify

ISG15 target proteins. Although I was unable to continue this work because of time, I would

have used tagged ISG15 to identify HCV viral proteins that are linked to ISG15.

USP18 is a specific protease that cleaves ISG15 from its targets. Previous studies demonstrated

that USP18 has a protease-independent function: binding to the type I IFN receptor IFNAR2

negatively regulates Jak/STAT signaling.2 More recently, USP18 has been found to be able to

188

modulate EGFR protein expression. 3

My previous work also indicates that USP18 promotes

HCV production and blunts IFN anti-HCV activity independently of its protease activity. 4

Taken together, USP18 may interact with host or viral proteins to modulate the anti-HCV

activity of IFN. USP18 interacting proteins identified from Tap-tagging/Mass spectrometry will

shed light on the role of USP18 in IFN resistance in HCV infected patients.

6.2. Understanding the cell-type specific expression of ISG proteins in hepatocytes Vs

macrophages:

It is very intriguing that differential gene expression in pre-treatment Rs and NRs derives from

different cell types. Immunohistochemical staining of pretreatment liver biopsies from HCV

infected livers with ISG15 and MxA antibodies indicated that the characteristic response

signature - elevated pretreatment expression of ISGs in the liver tissues, including ISG15 and

MxA -in NRs derived from hepatocytes while the reduced signal is came from macrophages in

Rs.5 This finding may change our traditional way of looking at chronic HCV infection.

Understanding why macrophage expression of ISGs correlated with R while ISG expression in

hepatocytes is associated with NR will help us further understand the role of ISG expression on

HCV replication/production and virus resistance to therapy.

6.3. Is there any link between the IL28B SNP and ISG expression in the liver?

Host genetic polymorphisms are an important factor in determining a patient‘s response to

treatment. In recent studies of patients with chronic HCV infections, SNP analysis was

employed to identify variants linked to both disease progression 8

and therapeutic response. 9,10

Several studies have reported associations between SNPs in the promoter regions of MxA,

189

OAS-1 and PKR and treatment outcomes.9,11,12

In a recent study of two patient cohorts, Huang

et al. demonstrated an important role between a polymorphism of IFN-γ (−764G, located in the

proximal I IFN-γ promoter region) and treatment response. 13

Yee et al. reported that two SNP

variants of IL10 (−592A and −819T SNP) were more frequently observed among the patients

with SVR compared to the non-responders to treatment with IFN/RBV. 14

Although these studies

did provide useful information in linking some SNPs to HCV treatment outcomes or disease

progression, the sample sizes were quite small, and cross-validation and whole genome scanning

is necessary.

Results from 4 different Genome-wide Association Studies (GWAS) indicated that a single-

nucleotide change roughly 3kb upstream of the IL28b gene promoter region, which encodes a

subtype of type III IFNλ3 , is associated with both treatment-induced and spontaneous Hepatitis

C Virus clearance. 15-18

Ge et al scanned the DNA sequences from more than 1600 treatment-

naïve HCV G1 infected patients and found that SNP rs12979860 is associated with SVR. 18

The

variation at this locus is either C or T. The authors then associated the C allele or T allele with

SVR in 3 ethnic populations (European Americans, African Americans, and Hispanics) and

found that if a given patient has the C allele, then the patient has a much higher chance of

achieving SVR as indicated in all 3 patient ethnic populations. It has been well known that

African Americans have a lower rate of SVR than European-Americans, so the authors compared

the distribution of C or T alleles in these 2 populations in relation to SVR and found that the

percentage of patients with the favourable C/C allele in African Americans is lower than in

European Americans, which may partially explain the difference in SVR rates between these 2

groups. It is also interesting to note that African Americans with the favourable C/C allele have

a higher SVR than European Americans with T/T, indicating that genetic background is more

190

important than race or ethnicity in determining the response rate. A similar finding was reported

by another group, indicating that this SNP is also associated with spontaneous clearance of the

virus.15

It is of note that, although SNP rs12979860 is indeed the strongest hit that was found to be

associated with treatment-induced or spontaneous HCV clearance from these GWAS, the authors

did find that other SNPs were also associated, albeit the association was statistically weaker.

From the top 100 SNPs that were associated with SVR, none of them replicated previous

reported SNPs ( MxA, OAS-1, PKR, IFNγ, and IL-10) that were associated with treatment

response.18

Failure to replicate these previous findings suggests that either those associations

were too weak to be picked up by GWAS or those previous SVR associated SNPs were

insignificant. To support this latter notion, from 637 non-Hispanic Caucasian patients infected

with genotype I, a study by Morgan et al. indicated that none of the 8 previously reported

treatment response-associated SNPs was found to be associated with SVR. 19

SNP rs12979860 was mapped roughly 3kb upstream of the IL28B gene promoter region, which

encodes a type III IFNλ3. Type III IFN (also called IFNλ), discovered 6 years ago20

, belongs to

the IL-10 super family with type I IFN-like functions. Three subtypes of IFNλs have been

reported: IFNλ1 is encoded by the IL29 gene, and IFNλ2 and IFNλ3 are encoded by IL28A and

IL28B, respectively. IFNλ signals through the type III IFN receptor, which consists of a

heterodimer of IFNλ specific IL28 receptor α-chain (IL28Rα) and IL10 receptor β

chain( IL10R2). These interferons, like type I IFN, activate the Jak/STAT signaling pathway.

Also like IFNα or IFNβ, the outcome of the activation by IFNλ is elevated expression of a set of

191

ISGs. Marcello et al 21

compared the effect of type I IFNs (IFNα or IFNβ) with type III (IFNλ)

on HCV replication in cell culture. Although the same sets of ISGs were induced by these two

types of IFNs and similar inhibitory effects were observed, the kinetics of ISG induction was

different: ISGs induced by type I IFNα or IFNβ peaked earlier but died down quickly, while

ISGs induced by type III IFNλ were delayed, but lasted longer. The mechanism and clinical

significance of the delayed induction of ISGs by IFNλ is not clear.

Another major difference in the action of type I IFNα or IFNβ and type III IFNλ is the cell-type

restricted expression of the IFNλ receptor. Unlike the ubiquitously expressed type I IFN

receptors (IFNAR1 and IFNAR2), type III IFN specific receptor IL28Rα chain is only expressed

abundantly on epithelial cells and on human hepatocytes (but not on murine hepatocytes). A

study by Ank et al 22

demonstrated that most cell types expressed both types I and III IFNs after

TLR stimulation or virus infection, whereas the ability of cells to respond to IFNλ was restricted

to a narrow subset of cells, including plasmacytoid dendritic cells and

epithelial cells. Taking all

these observations together, IFNλ function appears cell-type specific.

The fact that IFNλ induces the same set of ISGs and demonstrates similar anti-viral activities to

IFNα or IFNβ in cell culture and in various mouse models, and its action is restricted to a narrow

range of cell types (primarily epithelial cells and human hepatocytes ), suggests that IFNλ may

be more relevant than IFNα or IFNβ for treatment of HCV infection and might have fewer

adverse effects in the clinical setting.

The IL28b C/C genotype is associated with better response and spontaneous clearance, but it is

also associated with higher baseline viral load. 18

This is contradictory to the clinical

observation that lower baseline viral load predicts better response. How can we reconcile these

192

two seemingly contradictory observations? One possible explanation for this might be derived

from my previous studies on the HCV response signature. We and other groups found that

increased baseline hepatic ISG expression is associated with non-response to treatment. The ISG

expression in these NR livers is already maximized, and a much smaller elevation in expression

of these genes is observed compared to Rs following IFN treatment. This suggests that the IFN

or other related signaling pathways have already been activated in the NR livers following HCV

infection, leading to the enhanced expression of ISGs in the pre-treatment liver tissues.

It is quite plausible that the IL28B polymorphism has a role in the regulation of intrahepatic or

macrophage ISG expression, which alters viral load within those affected cell types and

eventually affects the treatment response. Indeed, I found that ISG15, one of the most abundant

ISGs induced by type I IFNs and viral infection, promotes HCV replication in cell culture. 1

Similarly, another ISG, USP18, the deconjugating enzyme for ISG15 conjugation pathway, also

stimulates HCV production and blunts IFN anti-HCV activity in vitro. 23

These data indicate that

preactivation of IFN signaling, possibly from activation of the type III IFN pathway, may be the

downstream effect of the IL28B SNP. I hypothesize that patients with the favourable IL28 C/C

allele have less activation of IFNλ signaling in the hepatocytes, leading to less elevation of the

hepatic ISGs and causing treatment response. On the other hand, patients with the less favourable

IL28 T/T allele have more profound activation of IFNλ signaling in the hepatocytes, leading to

increased expression of hepatic ISGs and causing treatment non-response.

In summary, data from previous research and the recent GWAS studies indicate that both type I

and type III IFN signaling pathways might be dysregulated by HCV infection. My previous

193

observation on the preactivation of these signaling pathways , leading to the enhanced expression

of hepatic ISGs in NRs, might be a downstream effect of the IL28B polymorphism. To link these

data together, functional studies detailing the mechanisms of the IL28B SNP association with

treatment response is needed, and connecting the restricted action of type III IFN on epithelial

cells and hepatocytes, together with the differential cell-type specific expression of ISGs

between R and NR, is required. It is conceivable that the IL28B SNP affects IFNλ expression,

leading to different activation states in hepatocytes while having no effects on macrophage cells

due to the restricted IL28B receptor expression pattern. This may cause the cell-type specific

ISG expression I observed that correlates with treatment response.

194

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