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Hepatic Gene Expression During Treatment with Peginterferon
and Ribavirin: Identifying Molecular Pathways for Treatment
Response
Jordan J. Feld1, Santosh Nanda1, Ying Huang1, Weiping Chen2, Maggie Cam2, Susan N.
Pusek3, Lisa M. Schweigler3, Dickens Theodore3, Steven L. Zacks3, T. Jake Liang1,*, and
Michael W. Fried3,*
1 Liver Diseases Branch, National Institute of Diabetes and Digestive and Kidney Diseases, National
Institutes of Health, Bethesda, MD
2 Microarray Facility, National Institute of Diabetes and Digestive and Kidney Diseases, National
Institutes of Health, Bethesda, MD
3 University of North Carolina, Chapel Hill, NC
Abstract
The reasons for hepatitis C treatment failure remain unknown but may be related to different host
responses to therapy. In this study, we compared hepatic gene expression in patients prior to and
during peginterferon and ribavirin therapy. In the on-treatment group, patients received either
ribavirin for 72 hours prior to peginterferon alpha-2a injection or peginterferon alpha-2a for 24 hours,
prior to biopsy. The patients were grouped into rapid responders (RRs) with a greater than 2-log drop
and slow responders (SRs) with a less than 2-log drop in hepatitis C virus RNA by week 4.
Pretreatment biopsy specimens were obtained from a matched control group. The pretreatment
patients were grouped as RRs or SRs on the basis of the subsequent treatment response. Gene
expression profiling was performed with Affymetrix microarray technology. Known interferon-
stimulated genes (ISGs) were induced in treated patients. In the pretreatment group, future SRs had
higher pretreatment ISG expression than RRs. On treatment, RRs and SRs had similar absolute ISG
expression, but when it was corrected for the baseline expression with the pretreatment group, RRs
showed a greater fold change in ISGs, whereas SRs showed a greater change in interferon (IFN)-
inhibitory pathways. The patients pretreated with ribavirin had heightened induction of IFN-related
genes and down-regulation of genes involved in IFN inhibition and hepatic stellate cell activation.
Conclusion—These data suggest that ISG inducibility is important for the treatment response and
that ribavirin may improve outcomes by enhancing hepatic gene responses to peginterferon.
Collectively, these mechanisms may provide a molecular basis for the improved efficacy of
combination therapy.
Despite great advances in the treatment of chronic hepatitis C infection, the current therapy
with peginterferon and ribavirin is effective in only about 50% of patients.1,2 The reasons for
treatment failure are not well understood but likely are related to both viral and host factors.3
Address reprint requests to: T. Jake Liang, Liver Diseases Branch, National Institute of Diabetes and Digestive and Kidney Diseases,
National Institutes of Health, Building 10, Room 9B16, 10 Center Drive, MSC 1800, Bethesda MD 20892-1800.
jakel@intra.niddk.nih.gov; fax: 301 402 0491.
*These authors contributed equally to this study.
Potential conflict of interest: Dr. Fried is a consultant for and received grants from Roche. Dr. Zacks is on the speakers’ bureau of Roche.
Supplementary material for this article can be found on the Hepatology Web site
(http://interscience.wiley.com/jpages/0270-9139/suppmat/index.html).
NIH Public Access
Author Manuscript
Hepatology. Author manuscript; available in PMC 2010 January 19.
Published in final edited form as:
Hepatology. 2007 November ; 46(5): 1548–1563. doi:10.1002/hep.21853.
NIH-PA Author Manuscript NIH-PA Author Manuscript NIH-PA Author Manuscript
The viral genotype has the greatest impact on the treatment outcome. A sustained virological
response (SVR) is achieved in 42%-46% of genotype 1 infections after a year of therapy, in
contrast to rates of 80% in genotype 2 and 3 infections after just 6 months of treatment.1,2 In
addition to the genotype, the baseline viral load is also an important factor, particularly in
genotype 1 infection.2 Although numerous viral strategies for interfering with host viral
defense mechanisms have been identified, none has been clearly shown to be responsible for
the genotypic differences in the treatment response.
From large treatment trials, a number of host factors have been found to be associated with the
treatment response. Gender and race are the most important factors. Men consistently respond
less well to therapy than women, and African Americans have poorer outcomes than Caucasian
populations.4 Age, obesity, and the degree of liver fibrosis also affect the treatment outcome.
5 Although these factors have been consistently identified in multiple studies, the mechanism
by which they affect the treatment outcome remains unknown.
To gain a further understanding of the host factors, microarray technology has been used to
evaluate hepatic gene expression prior to antiviral therapy. Chen et al.6 found that pretreatment
gene expression profiles from liver biopsies were predictive of the ultimate treatment outcome.
Patients who did not respond to therapy showed up-regulation of numerous interferon-
stimulated genes (ISGs) prior to treatment in comparison with both sustained responders and
normal controls. Hepatic gene expression has not been reported in humans undergoing therapy.
Several studies have reported gene expression in peripheral blood mononuclear cells (PBMCs)
during the course of therapy; however, gene induction in PBMCs may not be entirely reflective
of events in the liver.7–9
The addition of ribavirin to interferon (IFN) therapy significantly improves the treatment
response rates; however, the mechanism by which this occurs is poorly understood. Numerous
mechanisms of action for ribavirin have been proposed, including inosine-5-monophosphate
dehydrogenase inhibition (IMPDH), direct viral inhibition, increased mutagenesis leading to
error catastrophe, and promotion of a Th1 immune response.10 Although there is some
experimental evidence to support all of these mechanisms, none accounts for the magnitude
of the benefit seen with the addition of ribavirin.
In order to gain further understanding of the genetic factors that may contribute to the treatment
response, we evaluated gene expression from liver biopsy samples from patients currently
undergoing treatment with peginterferon. Half the patients also received ribavirin prior to liver
biopsy, and this allowed us to assess the contribution of this agent to gene expression.
Expression profiles from on-treatment patients were compared with those from pretreatment
liver biopsy samples of a matched control population.
Materials and Methods
Study Subjects
Adult patients evaluated at the University of North Carolina Liver Clinic and infected with
hepatitis C virus (HCV) genotype 1 were eligible for enrollment. Patients with a hepatitis B or
human immunodeficiency virus coinfection or a major systemic illness were excluded. All
patients received 180 μg of peginterferon alpha-2a subcutaneously 24 hours prior to liver
biopsy. The patients were randomized to receive peginterferon alone or to receive ribavirin
prior to biopsy as well. Those randomized to receive ribavirin were given weight-based
ribavirin (1000 mg/day for patients weighing less than 75 kg and 1200 mg/day for patients
weighing more than 75 kg) for 72 hours prior to liver biopsy. A sample of biopsy tissue was
snap-frozen in liquid nitrogen and stored at −80°C. The remainder of the biopsy was placed in
formalin for a histological evaluation. After the liver biopsy, the patients continued on 180
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μg of peginterferon per week plus weight-based ribavirin for 48 weeks (Fig. 1). All patients
agreed to undergo a liver biopsy after starting antiviral therapy and signed an informed consent
form. This investigator-initiated study was approved by the Institutional Review Board at the
University of North Carolina Medical Center.
The HCV viral load was measured with Roche Taq-Man (Roche Molecular Systems,
Alamaeda, CA) prior to therapy and serially during therapy. Those patients achieving at least
a 2-log drop in HCV RNA by 4 weeks of therapy were deemed rapid responders (RRs). Patients
with less than a 2-log drop in HCV RNA at 4 weeks were deemed slow responders (SRs).
Standard definitions for relapse (HCV RNA–negative at 48 weeks with subsequent recurrent
viremia), nonresponse [persistently HCV RNA–positive by polymerase chain reaction (PCR)
throughout therapy], and SVR (HCV RNA–negative 6 months after the completion of therapy)
were used to define the ultimate treatment outcome.
Stored liver tissue from pretreatment liver biopsies of patients with genotype 1 HCV infection
performed at the Clinical Center of the National Institutes of Health were used as pretreatment
controls. The same exclusion criteria were used for the pretreatment population, and patients
were selected to match on-treatment patients for gender, race, age, ultimate treatment outcome,
HCV genotype, baseline HCV viral load, and liver biopsy histological scores. Biopsy samples
were snap-frozen in liquid nitrogen at the time of biopsy and stored at −80°C. All but 4 of the
pretreatment patients were subsequently treated, and the on-treatment viral kinetics and
treatment response were known. The patients were categorized as RRs or SRs with the same
definitions used for the on-treatment group (rapid response ≥ 2-log drop in HCV RNA within
the first 4 weeks of therapy, slow response < 2-log drop by 4 weeks).
RNA Extraction, Amplification, and Microarray Analysis
Hepatic tissue was placed in Trizol and mechanically ground with a piston until it was
dissolved. RNA was then extracted with the RNeasy kit from Qiagen (Valencia, CA) according
to the manufacturer’s instructions. RNA was quantified with a spectrophotometer, and the RNA
quality was analyzed with an Agilent (Foster City, CA) bioanalyzer according to the
manufacturer’s instructions. RNA was then amplified with an Agilent Enzo kit. Amplified
complementary RNA was hybridized to an Affymetrix Human 133 Plus 2.0 microarray chip
containing 54,675 gene transcripts. The chips were scanned, and the signal intensity was
evaluated as previously described.11
Quantitative Real-Time PCR
A total of 14 genes were selected for real-time PCR confirmation and consisted of genes from
major pathways identified by the microarray analysis. The selected genes were divided into 4
categories: (1) IFN-related genes [IFN-alpha receptor 2, interferon regulatory factor 7 (IRF7),
ISG-15, signal transducer and activator of transcription 1 (STAT1), oligoadenylate synthetase
3 (OAS3), and myxovirus resistance 1 (Mx1)], (2) IFN-inhibitory genes [protein inhibitor of
activated signal transducer and activator of transcription 4 (PIAS4) and protein phosphatase
2a catalytic subunit (PP2Ac)], (3) apoptosis-related genes (Fas and cytochrome C), and (4)
genes related to hepatic stellate cell (HSC) activation [collagen type 1 alpha 2, CD36 (collagen
type 1 receptor), and tissue inhibitor of metallopeptidase 2 (TIMP2)]. Primers and probes were
obtained from Applied Biosystems (Foster City, CA). Real-time PCR was performed with
TaqMan technology as described.12 All values were normalized for the glyceraldehyde 3-
phosphate dehydrogenase expression level. All samples were repeated in duplicate, and mean
expression values were used.
TaqMan confirmation was performed for all patient samples with adequate remaining RNA
after the microarray analysis. In addition, a group of patient samples with inadequate RNA for
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the microarray analysis were included for real-time quantitative PCR. Samples analyzed only
by PCR were divided by the treatment group and response similarly to those used for the
microarray analysis. The gene expression patterns identified for confirmation by TaqMan were
based on the initial cohort for which there was sufficient RNA to perform the microarray
analysis. Because the latter group, for which only TaqMan was performed, was not included
in the original microarray analysis, it served as a validation cohort.
Statistical Analysis
The signal intensity from gene transcripts was compared between groups with Partek (St. Louis,
MO) software and Affymetrix (United States) MAS5 normalized log-signal comparisons.
Before the groups were compared, the signal-to-noise ratio was evaluated by a source of
variance analysis. This evaluates whether intergroup variation is greater than intragroup
variation across all comparisons. Further analysis was considered only for comparisons in
which between-group variation was significantly greater than within-group variation. For each
comparison, models were developed to evaluate the effects of race and sex as confounding
variables. The final selected model maximized the intergroup-to-intragroup differences or
signal-to-noise ratio by minimizing the error of the model. For example, when the treatment
response in the pretreatment group was compared, gender, race, and the interaction terms of
gender, race, and response were evaluated in the model. The inclusion of gender and the
interaction of gender and treatment response improved the model, giving an increased signal-
to-noise ratio. The addition of race had no effect on the model and thus was not included in
the final analysis. Similar model development was performed for all comparisons. After the
best model was selected, groups were compared with an analysis of variance. Gene expression
was compared between the treatment groups and by the treatment outcome. Only genes for
which a signal was detected in at least 50% of the samples were included. Expression
differences of at least 1.5-fold with P < 0.01 were considered significant. Because of the
significant risk of false positives with this type of analysis for individual gene expression,
known pathways were also compared with software from GeneGo, Inc. (Michigan). This
evaluates the gene expression of all genes in an established signal transduction or other
molecular pathway. The correction of an analysis of variance with Bonferroni or other
corrections for multiple comparisons generally assumes the complete independence of each
comparison. However, for a microarray analysis, this assumption is unlikely to be correct. If,
for example, 7 genes in a particular pathway are up-regulated or down-regulated, this is much
more likely to be a valid finding than if 7 random genes with unrelated functions are found to
be differentially regulated. Although all genes found by fold-change and P-value cutoffs are
listed, inferences were restricted to genes identified by a pathway analysis to be differentially
regulated. Once gene lists for each comparison were established, supervised hierarchical
clustering and heat maps were created with Partek software.
Results
Sufficient RNA with adequate quality for microarray analysis was available from 11 patients
pretreated with peginterferon with or without ribavirin (on-treatment group) and from 19
patients with biopsies prior to therapy (pretreatment group; Table 1). The groups were matched
by gender, race, and age, and all patients had a genotype 1 infection. The groups were also well
matched for the initial HCV viral load and histological grade and stage (Table 1). Six patients
in the on-treatment group achieved a rapid response (>2-log drop of HCV RNA by 4 weeks),
whereas 5 had a slow response. Six treated patients ultimately achieved an SVR (5 with a rapid
response and 1 with a slow response), 3 relapsed (all with a slow response), and 2 were lost to
follow-up (1 with a rapid response and 1 with a slow response). In the pretreatment group, 5
patients had a rapid response, 10 had a slow response, and 4 were treatment-naive; 5 patients
had an SVR (all with a rapid response), and 10 were nonresponders (all with a slow response).
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Six patients in the on-treatment group were treated with ribavirin in addition to peginterferon
prior to liver biopsy, 4 of whom went on to achieve SVR. The additional patients used for real-
time PCR for whom inadequate RNA was available for microarray analysis are described in
Table 1.
On-Treatment versus Pretreatment
Race and gender were found to contribute to the model for this comparison. After we controlled
for the effects of gender and race, 6017 genes were differentially regulated between the on-
treatment and pretreatment groups. Of genes that had detectable microarray signals in greater
than 50% of the samples, a total of 364 genes differed by greater than 1.5-fold expression with
a P value of less than 0.01. A summary of differing gene expression is shown in Table 2 and
highlighted in a heat map in Fig. 2. A full list of genes is available in the supplementary material
(Supplement to Table 2). Known ISGs were induced in the on-treatment group in comparison
with the pretreatment group. Classical ISGs such as OAS3, Mx1, and ISG15 and IRFs were
up-regulated. Other genes with known antiviral activities, including viperin, adenosine
deaminase RNA-specific, phospholipid scramblase, and apolipoprotein B messenger RNA
editing enzyme catalytic polypeptide-3A, were also induced in treated patients.13 ISGs provide
the effector antiviral functions of the IFN response. Genes involved in IFN production,
including IRF7 and retinoic acid–inducible gene I (RIG-I), were induced in the treated patients.
In addition to known ISGs, genes involved in the immune response, including interleukins,
chemokines, major histocompatibility complex class I, and nonspecific factors such as beta-2-
microglobulin and C-reactive protein, were also induced by peginterferon treatment. A
pathway analysis revealed that genes involved in antigen presentation, oxidative stress, and
apoptosis were also significantly up-regulated in on-treatment patients (Fig. 3). Although a
number of genes were also down-regulated by the treatment, aside from genes involved in
cellular proliferation such as the ras-oncogene family and tetraspanins, no clear pattern was
apparent.
Treatment Response
To evaluate whether pretreatment biopsies are of use for predicting treatment response, the
future SRs (same as nonresponders) and RRs (same as sustained responders) in the pretreatment
group were compared. The best model for this comparison included gender but not race. After
we controlled for the effect of gender, 2765 genes were differentially regulated, and 220 were
detectable in greater than 50% of the samples and differed by 1.5-fold or more with a P value
of 0.01 or less. As previously reported,6 nonresponders had significantly higher expression of
numerous ISGs than those who achieved an SVR when treated (Table 3 and Supplement to
Table 3).
Gene expression patterns in patients who received treatment prior to liver biopsy were
compared on the basis of their treatment response. Patients who achieved a 2-log or greater
drop in HCV RNA by 4 weeks of therapy were deemed RRs and were compared to SRs. Early
virological responses were compared rather than ultimate treatment outcomes to avoid issues
of compliance and treatment tolerance. In addition, we reasoned that the early responses might
be more reflective of altered gene expression by IFN than the ultimate treatment outcome.
There was a good correlation between the early response and late response, with 5 of 6 RRs
achieving an SVR. The other RR was lost to follow-up.
Similarly to the pretreatment samples, the best model for this comparison included gender but
not race. After we controlled for the effect of gender, 2884 genes were differentially regulated
between the RRs and SRs, and 179 genes were detectable in greater than 50% of the samples
and differed by 1.5-fold or more with a P value of less than 0.01. A summary of the differentially
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regulated genes is shown in Table 4, and a full list is available in the supplementary material
(Supplement to Table 4).
No significant differences in the expression of known ISGs were noted between the groups.
Genes involved in IFN-inhibitory pathways were up-regulated in SRs. PIAS1 interacts with
STAT1 and prevents binding to the interferon-sensitive response element (ISRE), resulting in
reduced ISG production.14 The ability of PIAS1 to interact with STAT1 is regulated by the
methylation status of STAT1. PP2A has been reported to interfere with the methylation of
STAT1, thereby increasing PIAS-STAT1 interactions and reducing STAT1-ISRE binding.
PP2A was elevated slowly in comparison with RRs. In addition, ubiquitin-specific peptidase
13 (USP13) was 2.7-fold more highly expressed in SRs. USPs cleave conjugated ubiquitin or
ubiquitin-like molecules from proteins, thus preventing them from being targeted for
degradation in the proteosome. USP18 has specifically been shown to cleave conjugated ISG15
from target proteins. The silencing of USP18 improves IFN responsiveness in vitro, and USP18
was found to be up-regulated in pretreatment liver biopsies from future nonresponders by Chen
et al.6 and in our cohort.15 Whether other USPs have similar IFN inhibitory activity is currently
unknown.
Although the absolute gene expression level is important, the magnitude of gene induction
from the baseline may also be relevant. Because patients could not be biopsied before and
during the treatment, true treatment-related gene induction could not be evaluated. However,
through the use of the pretreatment patients as the baseline for the on-treatment patients, a
surrogate for gene induction was assessed. Thus, RRs in the on-treatment group were compared
to RRs in the pretreatment group, and similarly, SRs were compared in each group. The fold
change was calculated by the division of the mean expression value of the on-treatment group
by the mean expression value of the pretreatment group because even after matching, a direct
comparison would be inappropriate because of interindividual variation in gene expression.
Because the pretreatment group and on-treatment group comprised different patients, this
comparison is not a true measure of gene induction but rather is a surrogate measure of hepatic
gene expressions in response to treatment. Using this comparison, after controlling for gender,
we found that RRs had 873 genes differentially regulated by 1.5-fold or more with a P value
of less than 0.01 between on-treatment and pretreatment groups, whereas SRs had 438 genes
that differed by the treatment group (Table 5 and Supplement to Table 5).
With this surrogate measure, the most striking difference between RRs and SRs was in the
IFN-related genes. Classical ISGs such as Mx1, OAS1-OAS3, ISG15, and viperin had a greater
fold change between on-treatment and pretreatment RRs than SRs, as did proteins involved in
the early IFN cascade such as STAT1. In addition, less well characterized ISGs (ISG20 and
IFN-induced protein 35) and other IFN-related genes (IFN-induced proteins with TTPR 1–5,
IFN transmembrane proteins 1 and 2, guanylate binding protein 1, and 28-kD IFN-responsive
protein) also showed a larger fold change between the on-treatment and pretreatment groups
among RRs. RIG-I and IRF7, which are involved in IFN production through the IRF3/7
pathway, also showed greater fold differences between treatment groups in RRs than SRs.16
This suggests that although the absolute level of expression of ISGs did not differ between the
RRs and SRs, there was a greater fold difference, a surrogate for greater induction of ISGs in
the RRs, and this may potentially account for their improved antiviral response.
In addition to IFN-related genes, several other pathways showed differences in the pretreatment
expression versus the on-treatment expression in a comparison of RRs and SRs. IFN inhibitory
pathways showed a greater fold-change difference in SRs than in RRs. PP2A expression was
6.6-fold higher (P = 0.0001) in on-treatment SRs versus pretreatment SRs but showed no
change among the pretreatment and on-treatment RRs. The small ubiquitin-like modifier
(SUMO) pathway was also affected. Like ubiquitin, SUMO binds to many proteins, targeting
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them for degradation, and it has been shown to bind STAT1, resulting in reduced IFN
responsiveness.17 SUMO-1 was down-regulated in RRs. USP18 showed a greater fold change
in RRs between pretreatment and on-treatment groups, and this likely reflected the fact that
this ISG was significantly up-regulated in the pretreatment SRs.
Some immune-related genes also differed by the fold change between the groups. IP10
[chemokine (C-X-C motif) ligand 10] expression was higher in pretreatment RRs versus on-
treatment RRs, whereas it was lower in on-treatment SRs. In studies of PBMCs, IP10 has been
found to be elevated prior to treatment, with down-regulation during treatment correlating with
viral clearance.18,19 Interleukin-6 was recently reported to be increased in HCV-infected
chimpanzees and was postulated to interfere with IFN signaling possibly through a suppressor
of cytokine signaling 3 (SOCS3)–mediated mechanism.20 Possibly in keeping with this
observation, the expression of the interleukin-6 receptor was markedly lower in on-treatment
RRs versus pretreatment RRs with no change in SRs. Other genes that differed between RRs
and SRs but were of less clear significance include the insulin receptor, the leptin receptor, and
insulin-like growth factor 1.
Ribavirin Versus No Ribavirin
Of the on-treatment patients, 5 received peginterferon alone, whereas 6 were treated with
ribavirin for 72 hours followed by peginterferon, prior to liver biopsy. These 2 groups were
compared to evaluate the independent effects of ribavirin on hepatic gene expression. Neither
race nor gender contributed to the model. A total of 3645 genes were differentially regulated
between the groups, and 563 genes differed by 1.5-fold or greater expression with a P value
of 0.01. A summary of the differentially regulated genes is shown in Table 6, and a full list is
available in the supplementary material (Supplement to Table 6). Gene expression patterns
differed in patients that received ribavirin in 4 main categories with potential relevance to the
treatment response. These included genes with effects on IFN signaling, IFN inhibition, HSC
activation, and apoptosis.
Patients treated with ribavirin and peginterferon showed greater induction of genes involved
in the IFN signaling cascade than those treated with peginterferon alone. The IFN-alpha
receptor was induced 1.4-fold in the ribavirin group, and although this did not meet the
predefined threshold for significance, the P value was highly significant (P = 0.001). This level
of induction early in the cascade may lead to important downstream effects. Similarly, IRF9,
which binds to STAT1 dimers to form IFN-stimulated transcription factor 3, the transcriptional
complex that leads to ISG production by binding to the ISRE,21 was up-regulated in ribavirin-
treated patients 1.3-fold with a P value of 0.0002. IRF7, critical for endogenous IFN production,
was also up-regulated in the ribavirin-treated patients.
In addition, ribavirin had effects on IFN-inhibitory pathways. PP2A was down-regulated in
the ribavirin-treated group; however, although there was a marked reduction in expression by
the fold change (−6.8), the P value (0.041) did not reach the 0.01 level of significance. When
evaluated by real-time PCR, PP2A was found to be statistically significantly down-regulated
in the ribavirin-treated patients (Fig. 4D). PP1C was also significantly down-regulated, and
although it has not been shown to directly interfere with STAT1 methylation, given its
homology with PP2A, it is possible that it has similar activity. SOCS1 also interferes with IFN
signaling.22 SOCS1 was down-regulated in the ribavirin-treated patients. Finally, the SUMO
pathway was affected by ribavirin. SUMO1, SUMO3, and the enzyme involved in SUMO
activation, SUMO-activating enzyme 1, were down-regulated by ribavirin; however, SUMO-
specific peptidase 3, which is responsible for SUMO degradation, was also down-regulated in
the ribavirin-treated patients. Overall, ribavirin down-regulated IFN-inhibitory pathways, thus
potentially enhancing the antiviral activity of IFN.
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The HSC is well established as the main cellular orchestrator of hepatic fibrosis.23 In response
to liver injury, the HSC undergoes a phenotypic change from a quiescent cell to a proliferative
myofibroblast-like activated cell that deposits extracellular matrix (ECM) leading to hepatic
fibrosis. Studies have identified numerous triggers for HSC activation. Treatment with ribavirin
led to down-regulation of a number of genes known to promote HSC activation. Transforming
growth factor beta (TGF-β) is one of the most potent stimuli for HSC activation and is also
produced by activated HSC.24 TGF-β1 and TGF-β3, the TGF-β receptor, and TGF-β receptor–
associated protein 1 were all down-regulated in ribavirin-treated patients. Activated HSCs
produce type I collagen.24 Both type I collagen and its receptor CD36 were down-regulated
by ribavirin treatment. Peroxisome proliferator-activated receptor gamma (PPAR-γ) is
expressed in HSC, and upon activation, PPAR-γ expression is reduced.23 In ribavirin-treated
patients, PPAR-γ expression was increased, supporting decreased HSC activation. Matrix
metalloproteinases degrade ECM deposited by HSC but are inhibited by the TIMP family. In
ribavirin-treated patients, matrix metalloproteinase 24 was induced, and TIMP2 was down-
regulated; this favored decreased ECM deposition. Ribavirin treatment resulted in the down-
regulation of genes involved in HSC activation (TGF-β family) and markers of HSC activation
(collagen type 1 and PPAR-γ), thus favoring less HSC activation and potentially less hepatic
fibrosis.
Ribavirin treatment also affected apoptosis pathways. Caspase 8, the main regulator in the
cytoplasmic Fas-associated death domain apoptosis cascade, was induced in ribavirin-treated
patients. Caspase recruitment domain family member 12, an important promoter of apoptosis,
was also up-regulated in the ribavirin group. There was a mix of up-regulation and down-
regulation among other genes affecting apoptosis. On balance, ribavirin treatment appeared to
promote apoptosis; however, given the complexity of the pathways, it is difficult to draw firm
conclusions.
Real-Time PCR Confirmation
In addition to the use of real-time quantitative PCR to confirm the microarray findings in the
original study group, tissue from patients for whom there was inadequate RNA to perform the
microarray was also evaluated. Because the patterns for PCR confirmation were established
only in those for whom the microarray was performed, the additional patients provided a
validation cohort. To increase the numbers, groups were compared by the combination of
patients from both cohorts, and the comparisons were performed within each group
individually as well (Table 1). Not all genes were compared in all patient samples because of
insufficient remaining RNA.
On-treatment patients in both the original and validation cohorts experienced the induction of
STAT1, Mx1, OAS3, ISG15, IRF7, and the IFN-alpha receptor in comparison with untreated
patients. Ribavirin-treated patients showed the induction of ISGs and the IFN-alpha receptor
in comparison with patients that received peginterferon alone. An examination of IFN-
inhibitory pathways showed that PP2A was down-regulated in ribavirin-treated patients.
Markers of HSC activation, including CD36, collagen 1A, and TIMP2, were all down-regulated
in ribavirin-treated patients. Fas was up-regulated in ribavirin-treated patients, and this
suggested increased apoptosis. There was inadequate RNA to evaluate other apoptosis markers.
Representative real-time PCR results are shown in Fig. 4.
Cluster Analysis
With the gene lists generated for each subgroup, cluster analysis was performed and
demonstrated that the groups separated into distinct gene expression groups, as shown in Fig.
5. Separation based on expression profiles was most distinct between pretreatment and on-
treatment patients and between those receiving ribavirin and those treated with peginterferon
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alone. Although patients did separate on the basis of the treatment response in both pretreatment
and on-treatment biopsy samples, the distinctness of the groups was not as great, as evidenced
by closer common branch points.
Discussion
In this study, peginterferon led to the hepatic induction of known ISGs and a large number of
other genes. The list of induced genes was compared to that from published data on IFN-treated
uninfected chimpanzees, primary human hepatocytes, and human PBMCs.7,9 A very similar
pattern of ISG induction was seen. As in PBMCs, transcription factors such as activating
transcription factor 7 and the proapoptotic interleukin-18 pathway were induced in the liver
with peginterferon treatment.9 Genes involved in IFN production, including IRF7 and RIG-I,
were also induced by treatment, and this suggested that therapeutic IFN may also promote
endogenous IFN production. Other pathways induced by treatment included those involved in
the oxidative stress response, apoptosis, and antigen presentation.
Although the lists of gene induced by peginterferon were similar between humans and
chimpanzees, the level of fold induction between treated and untreated patients was lower in
magnitude than the fold induction reported by Lanford et al.7 in chimpanzees. Most classical
ISGs were up-regulated 1.5–3–fold in on-treatment biopsies in comparison with pretreatment
samples, whereas Lanford et al. found induction levels of up to 47-fold. However, differences
in the study design may explain these apparent discrepancies. The chimpanzees were
uninfected at the baseline and served as their own controls. In contrast, in this study, the
pretreatment group comprised HCV-infected individuals. The induction of ISGs from
endogenous IFN is known to occur in chronically infected patients, and the elevation of the
baseline value will greatly reduce the fold induction even if similar absolute levels of gene
expression occur after treatment with IFN.7 With separation by the treatment response, this
difference became more apparent, with greater induction in RRs than SRs, likely because of
lower ISG expression at baseline in the future RRs. The timing of the evaluation may also be
important. Lanford et al. were able to biopsy chimpanzees sequentially and found that after an
initial surge in ISG expression at 4 hours, gene expression quickly dropped off within 24 hours.
Unfortunately, the time course of hepatic gene induction during therapy in naive or infected
humans is unknown, but it may be that there is a similar reduction in gene expression by 24
hours. A comparison of 24-hour gene expression after IFN between humans and chimpanzees
was fairly similar, with the difference in the baseline ISG expression from chronic HCV
infection in humans likely accounting for the differences.
Chen et al.6 previously reported that in pretreatment liver biopsies, nonresponders had higher
hepatic ISG expression than sustained responders or uninfected controls. An examination of
our pretreatment population revealed an identical pattern. All of the ISGs found by Chen et al.
to be differentially regulated were also noted in our analysis. In addition, a number of other
ISGs and IFN regulators, including STAT1 and RIG-I, were found to follow the same pattern
of increased expression in future nonresponders. This result confirms the original findings,
particularly given that not only were different cohorts examined but different microarray
platforms (complementary DNA versus RNA) were also employed. To ensure that our choice
of early virological response was reasonable, we analyzed the data on the basis of both early
and ultimate responses and found similar results (data not shown).
The comparisons of RRs and SRs offer some clues to understanding IFN nonresponse. The
first finding is that absolute ISG expression did not differ significantly between RRs and SRs
on therapy. Coupled with higher pretreatment ISG expression in future SRs, this raises the
question of whether SRs already have maximally induced ISGs and cannot respond further to
therapeutic IFN. To evaluate this issue, we compared ISG expression between SRs before and
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during treatment. For most ISGs, the mean expression level was higher in the on-treatment
SRs; however, this was not true for all ISGs. In addition, the magnitude of the difference was
small, and some individual patients had pretreatment gene expression levels comparable to
those found in on-treatment SRs. In contrast, among RRs, on-treatment ISG expression was
universally higher than pretreatment levels, and the fold change was much greater than that in
SRs. The effect on global gene expression also differed, with almost twice as many genes with
greater than 1.5-fold induction in RRs than SRs. Together, these data suggest that all patients
likely achieve maximal ISG induction on peginterferon, but SRs already have high and possibly
even maximal ISG expression prior to treatment. Although SRs may be able to induce ISGs
further with treatment, they gain little additional benefit from therapy, and this results in a slow
response and ultimately nonresponse.
Although ISG expression was similar between RRs and SRs on therapy, differences were seen
in IFN inhibitory pathways. PP2A levels are higher in patients infected with HCV than in
healthy controls, and both in vitro and in vivo expression of this protein results in
hypomethylation of STAT1, which results in greater interaction with PIAS1, thus reducing
STAT1-ISRE binding and subsequent ISG expression.14 PP2A expression was 8-fold greater
in SRs. Furthermore, in contrast to ISGs, IFN-inhibitory pathways showed a greater change in
the expression level between on-treatment and pretreatment SRs than RRs. The inhibition of
IFN activity may be critical for circumventing the effects of endogenous and therapeutic IFN.
A proposed schematic is shown in Fig. 6.
In contrast to the relative lack of ISG induction in the livers of SRs, ISGs were recently reported
to be strongly induced in PBMC from SRs, albeit somewhat less so than in RRs, after treatment
with IFN.25 The pattern of response to treatment among SRs is very similar to the pattern
recently described in HCV-infected chimpanzees. Huang et al.20 found that like human
nonresponders, chimpanzees had high baseline hepatic but normal PBMC ISG expression
levels. When they were treated with IFN, ISG induction in PBMCs from infected chimpanzees
was found to be at levels only slightly lower than those in naive animals. However, in the liver,
ISG induction was almost completely abrogated in the infected chimpanzees, and little or no
reduction in HCV RNA was seen. As in human SRs, increased expression of IFN-inhibitory
pathways was also seen in the infected animals. The similarities between these patterns of
response suggest that the chimpanzee may serve as a relevant model for understanding IFN
nonresponse.
Despite its clear effectiveness, the mechanisms by which ribavirin improves the response to
IFN are poorly understood. Taylor et al.9 found few differences in gene expression in PBMCs
in patients treated with ribavirin. In contrast, a comparison of hepatic gene expression in
patients receiving ribavirin and peginterferon with those receiving peginterferon alone revealed
differing gene expression patterns, potentially offering some important insights into the
mechanism by which ribavirin affects the treatment response. The most direct effect of ribavirin
was the induction of genes involved in the IFN cascade. In addition to increased expression of
the IFN-alpha receptor, IFN-stimulated transcription factor 3 and IRF7, which promotes
endogenous IFN production, were also induced in ribavirin-treated patients. The effect on IFN
signaling is in keeping with that reported by Zhang et al.,26 who found that ribavirin led to up-
regulation of ISGs and a reporter gene driven by the ISRE promoter in a respiratory syncytial
virus infection. Notably, in this experimental system, ribavirin increased ISRE activity only in
the setting of endogenous IFN production, with no effect seen with ribavirin treatment alone.
This is analogous to the situation seen in the treatment of HCV, in which ribavirin has little
effect as a monotherapy but leads to important synergistic improvements in treatment when
combined with peginterferon. The confirmation of the induction of IFN-alpha receptor and
IRF7 expression by real-time PCR in the cohort not examined by a microarray adds further
weight to the importance of this mechanism. This suggests that ribavirin may contribute to the
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antiviral response by making cells more responsive to IFN through the receptor and furthermore
by increasing the production of endogenous IFN. The down-regulation of IFN-inhibitory
pathways by ribavirin may further enhance the IFN response.
Aside from direct effects on the IFN response, ribavirin treatment altered gene expression in
pathways affecting HSC activation, which may reduce hepatic fibrogenesis. In addition to
known HSC activators, numerous other genes identified by microarray studies of HSC
activation were also down-regulated by ribavirin.23,24 This may account for the finding of
reduced hepatic fibrosis in a subset of patients treated with long-term ribavirin monotherapy.
27 Although the mechanism is unclear, increased hepatic fibrosis is associated with a poorer
treatment response.28,29 Perhaps by reducing fibrogenesis, at least in some patients, ribavirin
further improves IFN efficacy.
Ribavirin also appears to have an effect on apoptotic pathways. Although there were mixed
effects, caspase 8, the main activator of the Fas-mediated apoptosis pathway, was up-regulated
in ribavirin-treated patients. This is in keeping with a previous report from Schlosser et al.,30
who showed that IFN and ribavirin treatment of Hep G2 cells resulted in increased caspase 8
expression and increased Fas-mediated apoptosis. The finding that apoptotic pathways are also
up-regulated in responders to therapy suggests that apoptosis may be a critical component of
the response to HCV treatment.31
This study has some limitations. The sample size in all groups, particularly for ribavirin, is
relatively small; however, it is comparable to the size of many microarray studies. To provide
some validation, the cohort of patients with inadequate RNA for a microarray was evaluated
by real-time PCR with generally confirmatory results. The liver biopsies were performed 24
hours after the first dose of peginterferon. Up-regulation and down-regulation of IFN pathways
occur very rapidly, and even by 24 hours, some important effects may have been missed.7 In
addition, the lack of multiple biopsies also prevents the collection of longitudinal data on gene
expression, which may show important dynamic changes during the course of therapy. Use of
the pre-treatment group as a baseline for the on-treatment group is not ideal, but given the
infeasibility of biopsying patients before and during treatment, it is the best available
alternative. Despite the use of a P value of 0.01 for significance, the possibility of false positive
findings due to multiple comparisons remains. To minimize this issue, a fold-change threshold
was included, and signaling pathways and individual gene expression were also compared.
Finally, important findings were confirmed with real-time PCR.
In summary, we have shown that peginterferon treatment leads to the induction of known ISGs
and many other genes. We have confirmed previous findings showing increased ISG
expression in pretreatment liver biopsies of nonresponders. On treatment, we found that RRs
and SRs have similar ISG expression but SRs have up-regulation of IFN inhibitory pathways.
Evaluating a surrogate for treatment-induced gene induction by controlling for baseline
expression in the pretreatment group, we found that RRs have higher levels of induction of
ISGs, whereas IFN-inhibitory pathways are induced to a greater degree in SRs. Finally,
ribavirin appears to augment the IFN response and down-regulate genes involved in HSC
activation.
Supplementary Material
Refer to Web version on PubMed Central for supplementary material.
Acknowledgments
Supported in part by a grant from Hoffmann-La Roche, by a grant from the General Clinical Research Center of the
University of North Carolina (RR 000046), by a Midcareer Investigator Award in Patient-Oriented Research
Feld et al. Page 11
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(DK06614; to M.W.F.), Doris Duke Fellowship (LMS) and by the Intramural Research Program of the National
Institute of Diabetes and Digestive and Kidney Diseases (National Institutes of Health).
We thank Karen Dougherty, A.N.P., Roshan Shrestha, M.D., and George Poy for their contributions to this study.
Abbreviations
ECM extracellular matrix
HCV hepatitis C virus
HSC hepatic stellate cell
IFN interferon
IRF interferon regulatory factor
ISG interferon-stimulated gene
ISRE interferon-sensitive response element
Mx myxovirus resistance
OAS oligoadenylate synthetase
PBMC peripheral blood mononuclear cell
PCR polymerase chain reaction
PIAS protein inhibitor of activated signal transducer and activator of transcription
PP2A protein phosphatase 2A
PPAR-γperoxisome proliferator-activated receptor gamma
RIG-I retinoic acid–inducible gene I
RR rapid responder
SOCS suppressor of cytokine signaling
SR slow responder
STAT signal transducer and activator of transcription
SUMO small ubiquitin-like modifier
SVR sustained virological response
TGF-βtransforming growth factor beta
TIMP tissue inhibitor of metallopeptidase
USP ubiquitin-specific peptidase
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Fig. 1.
Study design. The patients in the on-treatment group were given either 180 μg of peginterferon-
alpha 2a subcutaneously 24 hours prior to liver biopsy or 1000/1200 mg of ribavirin daily for
72 hours plus 180 μg of peginterferon-alpha 2a subcutaneously 24 hours prior to liver biopsy.
After the biopsy, the patients were continued on the therapy for 48 weeks.
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Fig. 2.
Heat map showing distinct gene expression patterns in pretreatment and on-treatment groups.
The map is based on the 364 genes with a greater than 1.5-fold difference in the expression
with P <0.01. Red indicates increased gene expression, and blue indicates decreased gene
expression. The white line across and down the heat map provides a statistically significant
separation of the 2 groups and up-regulated or down-regulated genes. Within the 2 groups, the
treatment response is indicated as follows: N, naive treatment; RR, rapid responder; and SR,
slow responder.
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Fig. 3.
Gene pathways with significant differential gene expression between pretreatment and on-
treatment patients. For each known pathway, the number of genes with differing expression
between the groups was calculated, and a P value was determined on the basis of the likelihood
of finding the given number of genes by chance alone. The inverse logarithm of the P value is
shown on the x axis. Pathways with a cutoff of 2 (equivalent to a P value of 0.01) are shown.
In addition to interferon signaling, pathways involved in immune responses and apoptosis were
most affected by the interferon treatment.
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Fig. 4.
Analyses of the gene expression by a quantitative polymerase chain reaction. Selected genes
from the microarray analysis were quantified by a TaqMan real-time polymerase chain
reaction. (A) On-treatment versus pretreatment. (B) Future RRs versus SRs (pretreatment
group). (C) RRs versus SRs (on-treatment group by the fold change). Gene expression in the
on-treatment group was normalized for the baseline expression with the mean expression level
in the pretreatment group. The fold change was calculated as on-treatment RRs/pretreatment
RRs or on-treatment SRs/pretreatment SRs. (D) Peginterferon and ribavirin versus
peginterferon. RR indicates rapid responder; and SR, slow responder.
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Fig. 5.
Supervised hierarchical clustering by a gene list. The patients are categorized on the basis of
gene expression profiles. The number of branch points between patients reflects the degree of
similarity in the expression pattern. (A) On-treatment group versus the pre-treatment group.
(B) Future slow responders versus rapid responders in the pretreatment group. (C) Rapid
responders versus slow responders in the on-treatment group divided by the pretreatment
group. (D) Peginterferon plus ribavirin versus peginterferon alone.
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Fig. 6.
Postulated scheme of the importance of gene expression before and during peginterferon and
ribavirin treatment and its effect on treatment outcome. The strength of the adaptive immune
response and early endogenous IFN production likely determine whether the initial HCV
infection is cleared or becomes chronic. High ISG expression, presumably from increased
endogenous IFN production, is accompanied by up-regulation of IFN-inhibitory pathways.
This leads to reduced ISG induction with IFN treatment. Ribavirin enhances the efficacy of
IFN directly through up-regulation of the IFN receptor and indirectly through effects on
apoptosis and possibly HSCs. Ultimately, the degree of ISG induction with therapy may
determine treatment outcome. HCV indicates hepatitis C virus; HSC, hepatic stellate cell; IFN,
interferon; ISG, interferon-stimulated gene; RR, rapid responder; and SR, slow responder.
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Table 1
Baseline Characteristics of the On-Treatment and Pretreatment Groups Used for Microarrays and Quantitative
Real-Time PCR
Microarray Only Microarray and Real-Time PCR
On-Treatment
Group (n = 11) Pretreatment
Group (n = 19) On-Treatment
Group (n = 21) Pretreatment
Group (n = 31)
Gender (male/female) 5/6 10/9 13/8 19/12
Age 45.7 ± 6.9 51.7 ± 4.5 44.9 ± 5.3 51.6 ± 6.0
Ethnicity
African American 6 8 9 14
Caucasian 5 11 12 17
Treatment response
RR 6 5 10 10
SR 5 10 11 14
Naive NA 4 NA 7
Baseline HCV RNA
>6 log copies/mL 6 6 13 16
<6 log copies/mL 5 13 8 15
Biopsy stage*
03 3 7 4
15 10 10 18
23 1 3 4
30 5 1 5
40 0 0 0
Biopsy grade*
00 0 0 0
13 5 9 10
28 9 12 15
30 5 0 6
40 0 0 0
*The biopsy stages and grades follow the Batts and Ludwig scoring system. For RRs, there was a ≥2 log copies/mL decline in HCV RNA by 4 weeks
of treatment. For SRs, there was a <2 log copies/mL decline in HCV RNA by 4 weeks of treatment. Abbreviations: HCV, hepatitis C virus; NA, not
applicable; PCR, polymerase chain reaction; RR, rapid responder; SR, slow responder.
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Table 2
Selected Genes Induced by Peginterferon Treatment with a Fold Change Greater Than or Equal to 1.5 (P ≤ 0.01)
Gene Category Gene Name Gene Symbol Fold Change (On-Treatment/Pretreatment) P
ISG 2′,5′-Oligoadenylate synthetase 3 OAS3 2.2 0.0001
Adenosine deaminase, RNA-specific ADAR 1.5 2.6 × 10−7
Guanylate binding protein 1 GBP1 2.3 9.2 × 10−6
IFN transmembrane protein 1 (9–27) IFITM1 1.7 0.0009
IFN-alpha–inducible (ISG 15) ISG15 1.8 0.001
IFN-induced protein 35 IFI35 1.7 0.0001
IFN-induced protein 44 IFI44 2.4 3.8 × 10−5
IFN-induced protein with TTPR 3 IFIT3 1.7 0.004
IFN-induced protein with TTPR 5 IFIT5 1.5 3.1 × 10−5
Myxovirus resistance 1 Mx1 2.3 9.2 × 10−6
Myxovirus resistance 2 Mx2 2.5 5.9 × 10−7
Phospholipid scramblase 1 PLSCR1 3.0 4.6 × 10−8
Ubiquitin specific protease 18 USP18 1.8 0.0009
Viperin RSAD2 2.3 5.3 × 10−5
Apolipoprotein B messenger RNA
editing enzyme catalytic polypeptide-3A APOBEC3A 3.8 6.9 × 10−8
IFN-related IFN-alpha/beta receptor 2 IFNAR2 1.7 0.0001
IFN regulatory factor 7 IRF7 2.5 6.0 × 10−5
RIG-I DEAD box polypeptide 58 DDX58 1.6 0.0018
Signal transducer/activator transcription STAT1 2.3 1.2 × 10−6
Immune Beta-2-microglobulin B2M 2.3 3.2 × 10−5
Chemokine ligand 8 CCL8 4.6 1.16 × 10−8
Chemokine ligand 19 CCL19 2.6 0.005
C-reactive protein CRP 4.0 0.0032
Interleukin 18 binding protein IL18BP 1.8 2.6 × 10−5
Interleukin 6 signal transducer IL6ST 2.3 7.5 × 10−6
Major histocompatibility complex F HLA-F 1.5 0.0002
N-myc and STAT interactor NMI 1.7 2.8 × 10−6
Nuclear factor of activated T cells NFAT5 1.6 0.0002
Serum amyloid A1 SAA1 10.5 7.0 × 10−5
Abbreviations: IFN, interferon; ISG, interferon-stimulated gene.
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Table 3
Selected Genes Differing Between Future RRs and SRs with a Fold Change Greater Than or Equal to 1.5 (P ≤
0.01) in the Pretreatment Group
Gene Category Gene Name Gene Symbol Fold Change (RR/SR) P
ISG 2′,5′-Oligoadenylate synthetase 2*† OAS2 −2.4 0.02
2′,5′-Oligoadenylate synthetase 3*† OAS3 −2.4 0.01
IFN-induced transmembrane protein 1 IFITM1 −2.3 0.001
IFN-induced transmembrane protein 3 IFITM3 −1.5 0.004
IFN-alpha–inducible protein (ISG-15)*† G1P2/ISG15 −3.2 0.028
IFN-alpha–inducible protein (IFI-6–16)†G1P3/IFI6 −3.0 0.0016
IFN-alpha–inducible protein 27 IFI27 −3.7 0.004
IFN-induced protein 35 IFI35 −2.3 0.0048
IFN-induced protein 44 IFI44 −2.7 0.001
IFN-induced protein with TTPR 1†IFIT1 −2.8 0.01
IFN-induced protein with TTPR 3 IFIT3 −2.6 0.0075
Myxovirus resistance 1†MX1 −3.9 0.0004
Phospholipid scramblase 1 PLSCR1 −2.0 0.003
Ubiquitin-specific peptidase 18*† USP18 −1.7 0.042
Viperin†RSAD2 −3.3 0.0008
IFN-Related RIG-I DEAD box polypeptide 58 DDX58 −2.0 0.01
Signal transducer activator of
transcription 1 STAT1 −2.0 0.0048
Other Activating transcription factor 7
interacting protein 2†ATF7IP2 1.7 0.0083
Chemokine ligand 9 (MIG) CXCL9 2.9 0.0062
Leucine aminopeptidase 3*† LAP3 −1.5 0.030
Ribosomal protein S28†RPS28 1.98 0.0077
Syntaxin binding protein 5-like†STXBP5L 6.4 0.0003
*P between 0.05 and 0.01.
†Gene identified by Chen et al.6 as differing between future sustained responders and future nonresponders. Abbreviations: IFN, interferon; ISG,
interferon-stimulated gene.
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Table 4
Direct Comparison of Selected Genes Differing Between RRs and SRs with a Fold Change Greater Than or Equal
to 1.5 (P ≤ 0.01) in the On-Treatment Group
Gene Category Gene Name Gene Symbol Fold Change (RR/SR) P
IFN-inhibitory Protein phosphatase regulatory
subunit (formerly 2A) PPP2R3A −8.4 0.0004
Protein phosphatase 3 catalytic
subunit (formerly 2B) PPP3CB −2.4 0.0005
Ubiquitin-specific peptidase 13 USP13 −2.7 3.0 × 10−5
Other Insulin-like growth factor 1 IGF1 2.7 0.0025
Abbreviations: IFN, interferon; RR, rapid responder; SR, slow responder.
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Table 5
Comparison of a Surrogate for the Fold Induction of Selected Genes Differing Between RRs and SRs with a Fold Change Greater Than or Equal to 1.5 (P
≤ 0.01) in the On-Treatment Group Versus the Pretreatment Group (On-Treatment RRs/Pretreatment RRs versus On-Treatment SRs/Pretreatment SRs)
RRs (On-Treatment/Pretreatment) SRs (On-Treatment/Pretreatment)
Gene Category Gene Name Gene Symbol Fold Change PFold Change P
ISG 2′,5′-Oligoadenylate synthetase 1 OAS1 6.7 0.0004 1.0 NS
2′,5′-Oligoadenylate synthetase 2 OAS2 16.5 2.9 × 10−51.2 NS
2′,5′-Oligoadenylate synthetase 3 OAS3 8.8 0.0005 1.2 NS
28-kD IFN-responsive protein IFRG28 3.5 0.0003 NC NS
Adenosine deaminase, RNA-specific ADAR 1.8 1.5 × 10−6NC NS
Guanylate binding protein 1 GBP1 2.5 0.0003 NC NS
IFN-induced transmembrane protein 1 (9–27) IFITM1 5.4 0.0063 1.1 NS
IFN-induced transmembrane protein 2 IFITM2 1.7 0.0004 NC NS
IFN-induced with helicase C domain IFIH1 3.4 1.3 × 10−5NC NS
IFN-stimulated exonuclease gene 20Kda ISG20 4.6 0.0014 NC NS
IFN-induced protein 35 IFI35 4.6 0.0002 1.1 NS
IFN-induced 44-like IFI44L 57.9 0.0066 1.3 NS
IFN-induced protein with TTPR 1 IFIT1 6.0 0.0003 NC NS
IFN-induced protein with TTPR 2 IFIT2 5.9 0.0022 NC NS
IFN-induced protein with TTPR 3 IFIT3 5.8 0.0005 1.1 NS
IFN-induced protein with TTPR 5 IFIT5 3.0 0.0001 1.1 NS
IFN-stimulated gene 15 ISG15 13.6 0.005 1.6 0.014*
Myxovirus resistance 1 Mx1 17.7 0.006 1.1 NS
Myxovirus resistance 2 Mx2 5.7 2.7 × 10−51.6 NS
Viperin RSAD2 12.1 0.0007 1.9 1.1 × 10−5
IFN-related IFN-alpha 4 IFNA4 3.4 0.0070 NC NS
IFN receptor IFNAR2 NC NS 2.4 0.0011
IFN regulatory factor 7 IRF7 3.3 0.0040 1.2 NS
IFN-stimulated transcription factor ISGF3G 2.0 1.5 × 10−6NC NS
RIG-I DEAD box polypeptide 58 DDX58 2.7 5.8 × 10−7NC NS
Signal transducer activator of transcription 1 STAT1 5.2 0.0001 1.8 0.003
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RRs (On-Treatment/Pretreatment) SRs (On-Treatment/Pretreatment)
Gene Category Gene Name Gene Symbol Fold Change PFold Change P
IFN-inhibitory Protein inhibitor of activated STAT 2 PIAS2 NC NS 1.1*0.00001
Protein inhibitor of activated STAT 4 PIAS4 NC NS 1.2*5.4 × 10−5
Protein phosphatase 2 regulatory subunit B PPP2R2C NC NS 3.5 0.0002
gamma (formerly 2A)
Protein phosphatase 2 regulatory subunit B PPP2R3A NC NS 6.6 0.0001
alpha (formerly 2A)
Small ubiquitin-like molecule 1 SUMO1 −1.6 0.006 NC NS
Ubiquitin-specific peptidase 13 USP13 NC NS 2.3 0.0002
Ubiquitin-specific peptidase 18 USP18 5.33 2.5 × 10−6NC NS
Immune Chemokine (C-X-C motif) receptor 1 CCR1 2.0 0.0023 NC NS
Chemokine (C-C motif) ligand 14 CCL14 −1.6 0.0002 −2.8 0.0001
Chemokine (C-X-C motif) ligand 11 CXCL11 5.4 0.0008 NC NS
Complement component 1 subunit s1 C1S −7.1 3.9 × 10−6NC NS
Complement component 2 C2 −2.0 0.0041 NC NS
Complement component 9 C9 1.5 0.0013 NC NS
Interleukin 6 receptor IL6R −6.4 9.0 × 10−7NC NS
Interleukin 13 receptor alpha 1 IL13RA1 −2.0 3.9 × 10−5−1.1 NS
Interleukin 17 receptor B IL17RB −2.1 0.0007 −2.1 2.7 × 10−6
Interleukin 18 binding protein IL18BP 2.7 2.8 × 10−5NC NS
Other Insulin receptor INSR −2.0 2.4 × 10−5NC NS
Insulin-like growth factor 2 IGF2 −1.7 1.3 × 10−5NC NS
Integrin beta 1 ITGB1 −2.3 2.7 × 10−7NC NS
Leptin receptor LEPR −3.8 4.1 × 10−6−1.3 NS
*P between 0.05 and 0.01 or a fold change between 1.0 and 1.5. Abbreviations: IFN, interferon; ISG, interferon-stimulated gene; NC, no change in the gene expression detected between groups; NS, not
statistically significant; RR, rapid responder; SR, slow responder.
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Table 6
Selected Genes Induced by Ribavirin with a Fold Change Greater Than or Equal to 1.5 (P ≤ 0.01) in the On-
Treatment Group
Gene Category Gene Name Gene Symbol
Fold Change
(Ribavirin/
Peginterferon
Alone) P
IFN-related IFN-alpha/beta receptor 2*IFNAR2 1.4 0.0012
IFN regulatory factor 7*IRF-7 1.7 0.035
IFN-stimulated transcription factor 3*ISGF3 1.3 0.019
Janus kinase 1 JAK1 −2.8 0.0009
IFN-inhibitory Protein phosphatase 2A*PP2CA −6.8 0.042
Protein phosphatase 3 catalytic subunit
(formerly 2B) PPP3CA −1.8 0.003
Small ubiquitin-like molecule 1*SUMO1 −1.7 0.031
SUMO-1 activating enzyme*SAE1 −1.7 0.048
SUMO-1 peptidase 3 SENP3 −2.8 0.0065
Suppressor of cytokine signaling 1*SOCS1 −1.7 0.035
HSC CD 36 (collagen I receptor)*CD36 −2.7 0.047
Collagen type I alpha 2*COL1A2 −1.5 0.021
CREBBP/EP 300 inhibitor 1*CRI1 −2.0 0.027
Latent TGF-β binding protein 2*LTBP2 −1.4 0.0066
Kruppel-like factor 9 KLF9 1.9 0.0033
Matrix metallopeptidase 24 MMP24 4.2 0.0026
Peroxisome proliferator-activated
receptor gamma PPARGC1B 1.6 0.01
SMAD mothers against DPP homolog 4 SMAD4 −1.7 0.01
Tissue inhibitor of metallopeptidase 2*TIMP2 −1.7 0.031
Transforming growth factor beta 3 TGFB3 −3.2 0.0050
TGF-β receptor–associated protein TGFBRAP1 −3.9 0.0027
Apoptosis Apoptosis caspase activation inhibitor*AVEN 5.7 0.013
Apoptosis inhibitor 15*AP15 −1.7 0.032
Caspase 8*CASP8 1.8 0.047
BCL-2 associate athanogene BAG2 −2.8 0.0047
Caspase recruitment domain family 12 CARD12 4.1 0.010
P53-regulated apoptosis-inducing protein P53AIP1 −4.9 0.01
Programmed cell death 5*PDCD5 −1.3 0.005
Serine/threonine kinase 17b STK17B −3.9 0.01
TRAF2 and NCK interacting kinase TNIK −6.5 0.0078
*P between 0.05 and 0.01 or a fold change between 1.0 and 1.5. Abbreviations: HSC indicates hepatic stellate cell; and IFN, interferon.
Hepatology. Author manuscript; available in PMC 2010 January 19.
