predicting treatment response and the role of the isg15 ... · pathway to be involved in ifn...
TRANSCRIPT
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
Materials and methods 118
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
Materials and methods 149
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
Chapter 2: Search for a response signature in liver tissues of patients chronically
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
Chapter 2: Search for a response signature in liver tissues of patients chronically
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
Chapter 4-2: The role of ISG15 in HCV production
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
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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135. Kato T, Date T, Miyamoto M, Furusaka A, Tokushige K, Mizokami M, Wakita T. Efficient
replication of the genotype 2a hepatitis C virus subgenomic replicon. Gastroenterology
2003;125:1808–1817.
136.Kato T, Date T, Miyamoto M, Zhao Z, Mizokami M, Wakita T. Nonhepatic cell lines HeLa
and 293 support efficient replication of the hepatitis C virus genotype 2a subgenomic replicon. J.
Virol. 2005;79(1):592–596.
137. Wakita T, Pietschmann T, Kato T, Date T, Miyamoto M, Zhao Z, Murthy K, Habermann A,
Krausslich HG, Mizokami M, Bartenschlager R, Liang TJ. Production of infectious hepatitis C
virus in tissue culture from a cloned viral genome. Nat. Med. 2005;11:791–796.
138. Lindenbach B D, Evans MJ, Syder AJ, Wolk B, Tellinghuisen TL, Liu CC, Maruyama T,
Hynes RO, Burton DR, McKeating JA, Rice CM. Complete replication of hepatitis C virus in
cell culture. Science 2005;309:623–626.
139. Zhong J, Gastaminza P, Cheng G, Kapadia S, Kato T, Burton DR, Wieland SF, Uprichard
SL, Wakita T, Chisari FV. Robust hepatitis C virus infection in vitro. Proc. Natl. Acad. Sci. USA
2005;102:9294–9299.
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KP, Churchill TA, Lakey JR, Tyrrell DL, Kneteman NM. Hepatitis C virus replication in mice
with chimeric human livers. Nat. Med 2001; 7:927–933.
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
43
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
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
47
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.
48
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.
51
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
52
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.
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.
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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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 .
81
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).
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
503617 4283 CXCL9 chemokine (C-X-C motif) ligand 9 1.62 1.00E-004 0.014064712
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
491756 4283 CXCL9 chemokine (C-X-C motif) ligand 9 1.78 1.00E-004 0.045112863
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
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
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chronic genotype 2a or 2b hepatitis C virus infection. J Clin Gastroenterol. 1998;26(2):135-40.
109
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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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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.
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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
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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
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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,
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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
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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
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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
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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.
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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).
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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.
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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.
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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.
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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
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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
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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.
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Chapter 4-2: The role of ISG15 in HCV production
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]
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.
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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).
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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.
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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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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
176
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
179
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
References for chapter 5:
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2. Poynard T, Yuen MF, Ratziu V, Lai CL. Viral hepatitis C. Lancet 2003;362:2095-2100.
3. Glas Annuska M, Arno Floore, Delahaye Leonie JMJ, Witteveen Anke T, Pover Rob CF,
Niels Bakx, Lahti-Domenici Jaana ST, Bruinsma Tako J, Warmoes Marc O, René Bernards,
Wessels Lodewyk FA, Van 't Veer Laura J: Converting a breast cancer microarray signature
into a high-throughput diagnostic test. BioMed Central Genomics 2006, 278(7):2164-2167
4. Kane M, Jatkoe T, Stumpf C, Lu J, Thomas J, Madore S: Assessment of the sensitivity and
specificity of oligonucleo-tide (50 mer) microarrays. Nucleic Acid Research 2000,28(22):4552.
5. Hughes T, Mao M, Jones A, Burchard J, Marton M, Shannon K, Lefkowitz S, Ziman M,
Schelter J, Meyer M, Kobayashi S, Davis C, Dai H, He Y, Stephaniants S, Cavet G, Walker W,
West A, Coffey E, Shoemaker D, Stoughton R, Blanchard A, Friend S, Linsley P: Expression
profiling using microarrays fabricated by an ink-jet oligonucleotide synthesizer. Nature
Biotechnology 2001, 19(4):342-347.
6. Yuen T, Wurmbach E, Pfeffer RL, Ebersole BJ, Sealfon SC: Accuracy and calibration of
commercial oligonucleotide and custom cDNA microarrays. Nucleic Acids Research 2002,
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7. Barczak A, Rodriguez MW, Hanspers K, Koth LL, Tai YC, Bolstad BM, Speed TP, Erle DJ:
Spotted long oligonucleotide arrays for human gene expression analysis. Genome Research 2003,
13(7):1775-1785.
8. Carter M, Hamatani T, Sharov A, Carmack C, Qian Y, Aiba K, Ko N, Dudekula D, Brzoska P,
Hwang S, Ko M: In situ-synthesized novel microarray optimized for mouse stem cell and early
developmental expression profiling. Genome Research 2003, 13(3):1011-21.
9. Wang H, Malek R, Kwitek A, Greene A, Luu T, Behbahani B, Frank Buackenbush J, Lee N:
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13. Tan P, Downey T, Spitznagel EJ, Xu P, Fu D, Dimitrov D, Lempicki R, Raaka B, Cam M:
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14. Irizarry RA, Warren D, Spencer F, Kim IF, Biswal S, Frank BC, Gabrielson E, Garcia JG,
Geoghegan J, Germino G, Griffn C, Hilmer SC, Hoffman E, Jedlicka AE, Kawasaki E,
Martinez-Murillo F, Morsberger L, Lee H, Petersen D, Quackenbush J, Scott A, Wilson M, Yang
Y, Ye SQ, Yu W: Multiple-laboratory comparison of microarray platforms. Nat Methods 2005,
2(5):345-50
15. 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. 2009 Nov 6. [Epub ahead
of print]
16. Feld JJ, Nanda S, Huang Y, Chen W, Cam M, Pusek SN, et al. Hepatic gene expression
during treatment with peginterferon and ribavirin: identifying molecular pathways for treatment
response. Hepatology 2007;46:1548–1563.
17. Asselah T, Bieche I, Narguet S, Sabbagh A, Laurendeau I, Ripault MP, et al. Liver gene
expression signature to predict response to pegylated interferon plus ribavirin combination
therapy in patients with chronic hepatitis C. Gut 2008;57(4):516-24.
18. Yoneyama M., Kikuchi M., Natsukawa T., Shinobu N., Imaizumi T., Miyagishi M., Taira K.,
Akira S., Fujita T., The RNA helicase RIG-I has an essential function in double-stranded RNA-
induced innate antiviral responses, Nat. Immunol., 2004; 5(7):730—737
19. Andrejeva J., Childs K.S., Young D.F., Carlos T.S., Stock N., Goodbourn S., Randall R.E.,
The V proteins of paramyxoviruses bind the IFN-inducible RNA helicase, mda-5, and inhibit its
activation of the IFN-beta promoter, Proc. Natl. Acad. Sci. U.S.A., 2004; 101(49):17264-17269
20. Alexopoulou L., Holt A.C., Medzhitov R., Flavell R.A., Recognition of double-stranded
RNA and activation of NF-kappaB by Toll-like receptor 3. Nature 2001;413(6857):732-738
21. Darnell JE Jr. STATs and gene regulation. Science 1997; 277, 1630-1635.
22. Sadler AJ, Williams BR. Interferon-inducible antiviral effectors. . Nat Rev Immunol.
2008;8(7):559-68.
23. Kim, M.J., Hwang, S.Y., Imaizumi, T., Yoo, J.Y. Negative feedback regulation of RIG-I-
mediated antiviral signaling by interferon-induced ISG15 conjugation. J Virol 2008;82, 1474-83
24. Yuan, W. and Krug, R.M. Influenza B virus NS1 protein inhibits conjugation of the
interferon (IFN)-induced ubiquitin-like ISG15 protein. EMBO J. 2001;20:362–371
25. Zhao, C., Beaudenon, S.L., Kelley, M.L., Waddell, M.B., Yuan, W., Schulman, B.A.,
Huibregtse, J.M., and Krug, R.M. The UbcH8 ubiquitin E2 enzyme is also the E2 enzyme for
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ISG15, an IFN- /ß-induced ubiquitin-like protein. Proc. Natl. Acad. Sci. USA 2004;101, 7578–
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26. Kim, K.I., Giannakopoulos, N.V., Virgin, H.W., and Zhang, D.E. Interferon-inducible
ubiquitin E2, Ubc8, is a conjugating enzyme for protein ISGylation. Mol. Cell. Biol. 2004;24,
9592–9600
27. Zou W, Zhang DE. The interferon-inducible ubiquitin-protein isopeptide ligase (E3) EFP
also functions as an ISG15 E3 ligase. J Biol Chem. 2006 ;281(7):3989-94.
28. Wong JJ, Pung YF, Sze NS, Chin KC. HERC5 is an IFN-induced HECT-type E3 protein
ligase that mediates type I IFN-induced ISGylation of protein targets. Proc Natl Acad Sci U S A.
2006 ;103(28):10735-40
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removes ISG15 from conjugated proteins. J Biol Chem. 2002;277(12):9976-81.
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
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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-
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187
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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