Genomic scale analysis of racial impact on response
to IFN-α
Zoltan Posa,1, Silvia Sellerib, Tara L. Spiveya, Jeanne K. Wangc, Hui Liua, Andrea Worschechd,e, Marianna Sabatinof,
Alessandro Monacoa, Susan F. Leitmang, Andras Falush,i, Ena Wanga, Harvey J. Altera,1, and Francesco M. Marincolaa,1
a
Infectious Disease and Immunogenetics Section, Department of Transfusion Medicine, Clinical Center, and Center for Human Immunology, National
Institutes of Health, Bethesda, MD 20892; bSan Raffaele Telethon Institute for Gene Therapy, Scientific Institute H. S. Raffaele, Milan, 20132, Italy; cDivision of
Medical Imaging and Hematology Products, Office of Oncology Drug Products, Center for Drug Evaluation and Research, Food and Drug Administration,
Silver Spring, MD 20993; dGenelux Corporation, San Diego Science Center, San Diego, CA 92109; eVirchow Center for Experimental Biomedicine, Institute for
Biochemistry and Institute for Molecular Infection Biology, University of Würzburg, Würzburg 97074, Germany; fCell Processing Section, Department of
Transfusion Medicine, Clinical Center, National Institutes of Health, Bethesda, MD 20892; gBlood Services Section, Department of Transfusion Medicine,
Clinical Center, National Institutes of Health, Bethesda, MD 20892; hDepartment of Genetics, Cell and Immunobiology, Semmelweis University, Budapest 1089,
Hungary; and iInflammation Biology and Immungenomics Research Group, Hungarian Academy of Sciences, Semmelweis University, Budapest 1089, Hungary
Contributed by Harvey J. Alter, November 25, 2009 (sent for review October 23, 2009)
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African-American European American microarray
and activators of transcription hepatitis C virus
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C
| signal transducers
urrent treatment of chronic hepatitis C virus (HCV) infection relies on combinational therapy with the antiviral cytokine IFN-α and the antiviral nucleoside analog ribavirin. A major
factor affecting success of therapy is the HCV-infected hosts’
responsiveness to IFN-α. HCV-infected African-Americans
(AAs) exhibit limited responsiveness to IFN-α compared to
other races, such as European Americans (EAs), resulting in a
massively diminished AA:EA odds ratio for achievement of
sustained virological response (1–5). In line with this clinical
reality, peripheral blood mononuclear cells (PBMC) of HCVinfected AA patients display limited signal transducer and activator of transcription (STAT)1 phosphorylation compared with
EA patients and an altered gene expression profile upon stimulation with IFN-α ex vivo (2). These observations led to the
hypothesis that the geo-ethnic background of Americans might
impact IFN-α signaling upstream of STAT1 and compromise
responsiveness of AAs to therapy.
Interestingly, polymorphisms of genes involved in type I IFN
signaling or function have been associated with racial differences
in the outcome of chronic HCV infection (6). Furthermore,
www.pnas.org/cgi/doi/10.1073/pnas.0913491107
HCV infection suppresses IFN-α signaling, and HCV viral particles are capable of suppressing STAT1 transcription and
phosphorylation, decreasing its half-life and inhibiting nuclear
transport and DNA binding of activated STAT1 (7). Hence, in
HCV-infected individuals, it is difficult to conclusively test
whether the poor response of AAs to IFN-α/ribavarin treatment
and their impaired response to IFN-α ex vivo is due to a genetic
trait that directly affects IFN signaling or is secondary to a differential effect of HCV infection on the immune response of
AAs compared to EAs. Moreover, in HCV-infected patients it is
not possible to separate race-dependent markers of IFN-α
responsiveness from genetic polymorphisms indirectly affecting
resistance to viral infection or resistance to viral interference
with IFN-α signaling. In this study, we provide evidence that in
the absence of HCV infection, race does not affect ex vivo IFN-α
responsiveness. Racial traits affecting IFN-α treatment in HCV
infection may be restricted to polymorphisms in genes directly
interacting with or affected by HCV during the natural history of
the disease.
Results
IFN-α Signal Transduction Is Highly Conserved Between Healthy AA
and EA. STAT1 phosphorylation (STAT-P) is a key component of
the Janus kinase (JAK)-STAT signaling pathway correlated to
the activation of IFN-stimulated genes (ISGs) (8). Altered
phosphorylation of STAT1 in response to IFN-α stimulation of
PBMC in vitro was proposed as a predictor of poor responsiveness to therapy of HCV with the same agent particularly in
AAs (2). Therefore, we compared STAT-P induction between
healthy EAs and AAs. Pilot studies in our lab confirmed published reports (9) that among all PBMC subpopulations, T cells
and monocytes express STAT1 at the highest level and phosphorylate it most efficiently upon IFN-α stimulation (Fig. S1)
and that the 200 IU/mL concentration commonly used for
PBMC stimulation is roughly equivalent to that required for the
ED50 of STAT1 phosphorylation in T cells (ED50 IFN-α = 213 IU/
Author contributions: Z.P., S.F.L., E.W., H.J.A., and F.M.M. designed research; Z.P., S.S., T.L.S.,
J.K.W., H.L., and A.W. performed research; Z.P., T.L.S., M.S., A.M., E.W., and F.M.M. contributed new reagents/analytic tools; Z.P., E.W., and F.M.M. analyzed data; and Z.P., S.F.L.,
A.F., H.J.A., and F.M.M. wrote the paper.
The authors declare no conflict of interest.
Freely available online through the PNAS open access option.
Data deposition: The data reported in this paper have been deposited in the Gene Expression Omnibus (GEO) database, www.ncbi.nlm.nih.gov/geo (accession no. GSE17952).
1
To whom correspondence may be addressed at: Infectious Disease and Immunogenetics
Section, Department of Transfusion Medicine, Clinical Center, National Institutes of Health,
Building 10, R1C711, 9000 Rockville Pike, Bethesda, MD 20892. E-mail: [email protected].
nih.gov, [email protected] or [email protected].
This article contains supporting information online at www.pnas.org/cgi/content/full/
0913491107/DCSupplemental.
PNAS | January 12, 2010 | vol. 107 | no. 2 | 803–808
IMMUNOLOGY
Limited responsiveness to IFN-α in hepatitis C virus (HCV)-infected
African-Americans compared to European Americans (AAs vs. EAs)
hinders the management of HCV. Here, we studied healthy nonHCV-infected AA and EA subjects to test whether immune cell
response to IFN-α is determined directly by race. We compared
baseline and IFN-α-induced signal transducer and activator of transcription (STAT)-1, STAT-2, STAT-3, STAT-4, and STAT-5 protein and
phosphorylation levels in purified T cells, global transcription, and
a genomewide single-nucleotide polymorphism (SNP) profile of
healthy AA and EA blood donors. In contrast to HCV-infected individuals, healthy AAs displayed no evidence of reduced STAT activation or IFN-α-stimulated gene expression compared to EAs.
Although >200 genes reacted to IFN-α treatment, race had no
impact on any of them. The only gene differentially expressed
by the two races (NUDT3, P < 10−7) was not affected by IFN-α
and bears no known relationship to IFN-α signaling or HCV pathogenesis. Genomewide analysis confirmed the self-proclaimed
racial attribution of most donors, and numerous race-associated
SNPs were identified within loci involved in IFN-α signaling,
although they clearly did not affect responsiveness in the absence
of HCV. We conclude that racial differences observed in HCVinfected patients in the responsiveness to IFN-α are unrelated to
inherent racial differences in IFN-α signaling and more likely due
to polymorphisms affecting the hosts’ response to HCV, which in
turn may lead to a distinct disease pathophysiology responsible
for altered IFN signaling and treatment response.
mL; Fig. S2). On the basis of this observation, the critical role of
T cells in resolving HCV infection (10), and their relative
abundance compared to monocytes, we used T cells purified by
negative selection.
In contrast to the marked differences observed in HCVinfected patients (2), STAT1-P fold change (FC) upon IFN-α
stimulation was not significantly different between healthy AA
and EA donors (Fig. 1B), nor were baseline and induced STAT1P levels or levels of STAT1 protein (Fig. 1C). In addition, race
did not affect responsiveness as measured by ratios of STAT1-P
or STAT1-positive cells in control and IFN-α-affected samples
(Fig. S3). The only difference observed between the two ethnic
groups was intraracial response heterogeneity, which was found
to be greater in AAs, compared to EAs. By arbitrarily splitting
the response phenotype into low, medium, and high (STAT1-P
FC <1.5, 1.5–3.0, and >3.0, respectively), AA values scattered in
a uniform distribution among all categories, whereas EA values
prevailed in the medium response group (Fig. 1B, P = 0.028, χ2test). In smaller substudies comparing 12 donors from each of
the two races, we evaluated all other STAT proteins known to be
affected by IFN-α in T cells (STAT2, STAT3, STAT4, and
STAT5). We found no significant differences between races in
baseline or induced STAT phosphorylation levels, STAT protein
levels (Fig. 1 B and C), or the ratio of STAT or STAT-P positive
cells (Fig. S3). Moreover, baseline and IFN-α-induced STAT
protein phosphorylation levels and phosphorylation fold changes
were highly variable and showed generally poor correlation
among STAT proteins (with the exception of STAT1-P and
STAT3-P; R2 = 0.522, P value <0.001), clearly challenging the
assumption that STAT1 phosphorylation can exclusively represent the complexity of the IFN-α response ex vivo (Fig. 1 A
and D).
To test whether the lack of racial difference in IFN-α-induced
STAT1-P in healthy individuals was limited to IFN-α, T cells
were treated with cytokines using signaling mechanisms similar
to IFN-α (IFN-β), highly similar intracellular signaling but distinct receptor usage (IFN-λ), overlapping at one or more levels
of the JAK/STAT pathway (IL-2 and IFN-γ), or, as a negative
control, unrelated to IFN-α signaling (IL-4). No race-associated
difference in induced STAT1 phosphorylation was observed
regardless of the cytokine tested (Fig. 2A). However, we
observed that induced STAT1-P FC was highly correlated among
cytokines, underlining the limited specificity of STAT1-P FC as a
marker of in vitro IFN-α responsiveness (Fig. 2 B–E). Finally, in
line with others’ observations (11), we observed that CD3+ T
cells’ in vitro responsiveness to IFN-λ is very limited (Fig. 2A).
Fig. 1. No difference between healthy EAs’ and AAs’ IFN-α-induced STAT1, STAT2, STAT3, STAT4, and STAT5 activation. Activity of STAT1, STAT2, STAT3,
STAT4, and STAT5 signal pathways is displayed, as affected by IFN-α and race. (A) Representative flow cytometry data for each STAT protein. (B) IFN-α-induced
fold change in STAT protein phosphorylation. (C) STAT protein levels (STAT Prot), baseline (Ph C), and IFN-α-induced (Ph IFNa) STAT phosphorylation levels.
Median is indicated by horizontal lines, and P values obtained from Student's t-tests or the Mann–Whitney rank sum test, as appropriate, are shown above
sample groups. (D) Correlation between STAT1 and other STAT proteins’ phosphorylation in the same samples along with R and P values obtained from
Pearson's correlation analyses.
804 | www.pnas.org/cgi/doi/10.1073/pnas.0913491107
Pos et al.
tion, antigen presentation, and intracellular recognition of viral
pathogens, etc. (Table 2).
In contrast, race was found to have virtually no impact on the
gene expression profile of the analyzed samples, as there was
only one transcript that was both significant and reproducibly
different between races. The only transcript affected by race was
NUDT3, whose expression was decreased in AAs. NUDT3 is a
gene involved in nucleoside metabolism, playing a role in the
removal of some toxic nucleotide metabolites, expressed almost
ubiquitously in all human tissues, but with no known relationship
to HCV infection, IFN-α signaling, antiviral responses, or other
immune-related processes. Hence, ethnic differences in NUDT3
expression are unlikely to explain the differential response to
IFN-α of HCV-infected individuals. In summary, our data suggest a marked dissociation between the number of race-affected
genes compared to IFN-α-induced genes in healthy donors.
Interestingly, this dissociation remained highly consistent
regardless of statistical selection criteria of DEGs (Fig. 4A).
Moreover, genes potentially affected by race did not overlap
with IFN-α-affected genes at any FC or α-levels chosen for
analysis (Fig. 4A); in addition, mixed-effects model ANOVA
confirmed that there were no genes affected by an interaction
between race and IFN-α treatment (genes expressed at different
levels depending on race only before or after IFN-α treatment)
at any α-levels and FC criteria used (Fig. 4A). In contrast to
HCV-infected patients, race did not affect average fold change
of ISGs in healthy donors (Fig. 4B). As opposed to IFN-α
treatment, race had virtually no effect on the ISG expression
profile as judged by unsupervised hierarchical clustering or
multidimensional scaling (Fig. 4 C and D).
Fig. 2. Healthy EAs and AAs do not differ in activation of STAT1 by various
cytokines, using signal transduction pathways overlapping with IFN-α. (A)
Fold change STAT1 phosphorylation induced by various cytokines capable of
activating STAT1, as indicated, with IL-4 serving as negative control. (B–E)
Correlations between STAT1 phosphorylation induced by various cytokines
along with R and P values obtained from Pearson's correlation analyses.
Healthy Individuals Exhibit Activation of ISGs Regardless of Race.
Global gene expression profiling was used to identify genes whose
expression could be affected by race, IFN-α treatment, both race
and treatment, or interactions between race and treatment. To
optimize the trade-off between size and reproducibility/reliability
of the list of differentially expressed genes (DEGs), several fold
change cutoff values (FC >1, >1.5, and >2.0 in >25% of all samples) and various P-value criteria (P < 10−3–10−9) were tested by
analyzing a “training” array set (Table 1), using mixed-effects
model ANOVA. The resulting gene lists were retested on a second, nonoverlapping “test” array set (Table 1) for their reproducibility, i.e., their ability to reproduce sample separation by major
experimental factors (race, treatment) in an independent sample
set by unsupervised hierarchical clustering.
In general, gene sets defined by FC >1.5 and P < 10−7 were
found to be the most reproducible at this sample size and
composition (156 arrays total), while still containing the most
comprehensive DEG list. Using these criteria, IFN-α treatment
affected 222 transcripts (205 annotated, Fig. 3A, see Table S1 for
detailed annotation and expression data). Pathway analysis of
genes significantly affected by IFN-α treatment confirmed functional reliability of these findings, disclosing that these ISGs
constitute canonical gene networks involved in well-known IFNaffected processes, such as IFN-α signaling, protein ubiquitina-
expression following IFN-α stimulation was consistent among
individuals of either race, clear heterogeneity was observed in
the expression of a subgroup of ISGs in baseline conditions
(unstimulated T cells). As in chronic HCV, pretreatment activation of prominent classical ISGs and canonical ISG pathways
has been reported to be associated with nonresponse to IFN-α
therapy (12, 13). We decided to further analyze unstable ISGs in
detail. In contrast to HCV-infected patients, this subgroup of
transcripts (further referred to as baseline unstable ISGs, Fig. 3
A and B) included genes whose function is not directly associated
with canonical IFN-regulated pathways, whereas the rest of the
ISGs (designated as baseline stable ISGs) were selectively
regulated by IFN-α stimulation (see Table S2 for lists of baseline
stable and unstable ISGs). Comparative analysis of the training
and test sample sets by hierarchical clustering confirmed that this
phenomenon affected a reproducible set of genes in both the
training and the test sample sets analyzed (Fig. 3 A and B).
Ingenuity pathway analysis (IPA) suggested that baseline
unstable ISGs of healthy individuals covered different aspects of
cellular metabolism, in sharp contrast with baseline stable ISGs
Table 1. Summary of donor numbers, age, race, and gender distribution in all experimental sample sets analyzed in this study
Sample Total no. Size African: No.
set
donors (n)
(% total)
Master
Training
Test
Repeat
78
39
39
15
156
78
78
60
37
18
19
9
(47.4)
(46.2)
(48.7)
(60.0)
Age, years: AA male: No. AA female: No. European: No. Age, years: EA male: No. EA female: No.
Mean ± SEM
(% total)
(% total)
(% total)
Mean ± SEM
(% total)
(% total)
45.9
47.9
44.0
45.1
±
±
±
±
1.5
2.2
2.1
2.0
30
14
16
8
(38.5)
(35.9)
(41.0)
(53.3)
7
4
3
1
(8.9)
(10.3)
(7.7)
(6.7)
41
21
20
6
(52.6)
(53.8)
(51.3)
(40.0)
49.1
47.8
51.0
45.1
±
±
±
±
2.3
3.2
3.4
2.0
32
16
16
6
(41.0)
(41.0)
(41.0)
(40.0)
9
5
4
0
(11.5)
(12.8)
(10.3)
(0.0)
The master set includes all donors analyzed in this study, and the training and test sets represent two nonoverlapping halves of the master set; all donors of
the master, training, and test sets are represented by one single blood donation in all analyses. Blood donations of the third repeat set were derived from
donors donating twice in two random independent time points over a period of 1 year; in this set, each donor is represented by two donations. Statistical
group (sample) size of sample sets (n) is equal to (the number of donors) × (number of donations/donor) × (2), as each donation has been split into a control
and an IFN-α–treated parallel in all analyses.
Pos et al.
PNAS | January 12, 2010 | vol. 107 | no. 2 | 805
IMMUNOLOGY
IFN-α-Independent Activation of a Subgroup of ISGs Accounts for
Baseline Heterogeneity Among Healthy Donors. Whereas ISG
Fig. 3. Characterization of IFN-α-stimulated genes, assessment of response heterogeneity, and stability of individual differences in healthy EAs and AAs. (A)
IFN-α-stimulated genes (ISGs) identified by microarrays and mixed-effects model ANOVA (P > 10−7, FC >1.5) in a “training” sample set consisting of 78
samples. (B) The same ISG set is tested for reproducibility by unsupervised hierarchical clustering of an independent “test” sample set of 78 samples, collected
from different individuals. Untreated controls are labeled with purple and IFN-α-treated samples with pink bars. Major heterogeneity between individuals is
shown in the form of ISGs displaying frequent, IFN-α-independent activation in the control groups. These genes, designated as baseline unstable ISGs, are
shown marked with yellow bars. (C) Limited stability of this phenomenon over time in a third, “repeat” sample set consisting of samples derived from
identical donors at two different time points. Samples derived from the same individuals are shown color coded.
clearly associated with canonical IFN-α signaling pathways such
as protein ubiquitination, antigen presentation, and JAK-STAT
signaling, etc. (Table 2). Hence, gene expression analysis suggests that immunity-related core IFN-α functions are relatively
homogenously activated by IFN-α in healthy individuals and are,
in general, inactivated in baseline conditions. Most individual
heterogeneity is present in genes associated with diverse nonTable 2. Functional distribution of all baseline stable and
unstable ISGs among canonical Ingenuity pathways
−Log(P)
Ratio
IFN signaling
Activation of IRF by cytosolic PRRs
Polyamine regulation in colon cancer
Protein ubiquitination pathway
Antigen presentation pathway
Growth hormone signaling
Prolactin signaling
RIG1-like receptors in antiviral responses
10.60
5.02
3.62
2.66
2.17
2.12
2.01
1.80
0.31
0.09
0.09
0.03
0.08
0.05
0.05
0.06
Baseline stable ISGs
IFN signaling
Activation of IRF by cytosolic PRRs
Protein ubiquitination pathway
Polyamine regulation in colon cancer
Prolactin signaling
Antigen presentation pathway
JAK/STAT signaling
Gly, Ser, and Thr metabolism
13.10
6.89
4.35
3.66
3.01
2.96
2.19
2.15
0.31
0.09
0.03
0.07
0.05
0.08
0.05
0.02
1.54
1.42
1.33
1.22
1.20
1.19
1.16
1.15
0.04
0.01
0.02
0.02
0.02
0.02
0.02
0.01
All ISGs
Baseline unstable ISGs
CNTF signaling
Pyrimidine metabolism
Inositol metabolism
Dopamine receptor signaling
Ceramide signaling
Fatty acid biosynthesis
Phospholipid degradation
Purine metabolism
−Log(P value) indicates significance of association with a canonical Ingenuity pathway; the eight most significant pathways are shown. Ratio values
are calculated by dividing the number of genes that meet cutoff criteria by
the total number of genes that make up the pathway.
806 | www.pnas.org/cgi/doi/10.1073/pnas.0913491107
immune cellular functions exerted by IFN-α, predominantly
those related to metabolic processes. We next analyzed whether
preactivation of the baseline unstable ISG module was a stable
phenotype for a discrete set of individuals. Using the repeat
sample set, consisting of repeated collections derived from the
same donors at different time points, we observed that activation
of baseline unstable ISG was not idiosyncratic and could vary at
different donations from the same individual over time (Fig. 3C);
thus, baseline variations in unstable ISGs are also unlikely to be
genetically determined.
We found that STAT1-P FC showed very limited correlation
with ISG expression. Individuals with low, medium, and high
STAT1-P FC values presented some limited coclustering, but no
striking separation by ISG expression patterns (Fig. 4C). Consistent with this, multidimensional scaling revealed that lower
numbers of STAT1-P-positive cells usually lead to weaker
responses at the ISG induction (P < 0.001); however, generally
this marker was not able to predict the response at the ISG level
(Fig. S4).
Whole-Genome SNP Profiling Confirms Racial Identity and Discloses a
List of Race-Associated Polymorphisms of ISGs. Whole-genome SNP
profiling was used to analyze the distribution of SNP patterns
between EAs and AAs, with particular focus on IFN-α-related
genes. It has been shown recently that Africans residing in Africa
can be heterogeneous at the SNP level, showing distinct separation from other non-African ethnic groups, whereas AfricanAmericans have a more homogeneous West African origin and
are often admixed to non-African populations (14). Thus, a
confirmation of the self-proclaimed ethnicity of individual
donors was deemed necessary to enhance the accuracy of our
study. Whole-genome SNP arrays corresponded accurately to the
subjective racial self-identification of donors; analysis of 848,365
SNPs that passed instrumental and methodological quality control (QC) criteria (of ∼960,000 present in the array platform)
clustered donors according to their self-proclaimed race with
only two exceptions (Fig. 5A, two self-proclaimed AAs clustering
with EAs). Finally, DNA samples of blood donors whose samples
were obtained on two different occasions clustered together and
displayed a virtually identical genotype in repeat samples
(Fig. 5B).
Race bore a huge impact on the SNP profile, resulting in
the identification of 26,026 SNPs specific to the AA population,
more or less evenly distributed along the genome (Fig. 5C)
[as 848,365 SNPs passed the QC filters, the P value after the
Pos et al.
Bonferroni correction for multiple testing (0.05/848,365) was P <
5.89 × 10E−8; see Dataset S1 for a full list of SNPs linked to race].
Concordantly, of the 5,320 SNPs linked to the 222 ISGs identified
by gene expression profiling, many were affected by race (see full
list of 158 SNPs associated to both race and ISGs in Dataset S2).
These findings are remarkable, considering that in spite of the
relatively large number of race-linked SNPs in ISGs, none had a
significant racial impact on the mRNA-level expression of its
associated gene.
Finally, it was proposed that chromosome 19 contains a hotspot
of several SNPs strongly affecting IFN-α responsiveness in chronic
HCV infection and spontaneous HCV clearance (15–17), most of
them associated to the IL28B/A (also known as IFN-λ 3/2) region;
thus, additional analysis was performed focusing on this region.
Although race-linked SNPs are numerous, and evenly distributed
on chromosome 19 (Fig. 5D), we did not find significant differPos et al.
Fig. 5. Identification of SNPs linked to EA and AA genetic background, ISGs,
and IFN-α response by whole-genome SNP analysis. (A) Efficient separation of
EA and AA donors according to self-proclaimed race by unsupervised hierarchical clustering of 848,365 SNPs; self-proclaimed race is shown by color code
(pale blue, European; yellow, African-American). (B) Accuracy of genomicscale SNP profiling by showing virtually identical SNP patterns obtained from
repeated donations (labeled a–e). (C) Distribution of 26,026 SNPs significantly
linked to AA background on all human chromosomes (Dataset S1), including
158 race-linked, ISG associated SNPs (Dataset S2). (D) Significantly race-linked
SNPs on chromosome 19, which contains a suspected hotspot of IFN-α
response. (E) None of the SNPs detected in the hotspot region (blue frame) was
significantly linked to race. In C–E, the y axis indicates uncorrected P values,
whereas horizontal red lines indicate Bonferroni-corrected significance
thresholds, equivalent to a global P value of P < 0.05.
ences between healthy EAs and AAs in the SNPs genotyped in the
IFN-λ 3/2 region (Fig. 5E).
Discussion
Our study shows that previously reported racial differences in
responsiveness to IFN-α under pathological conditions are not
present in healthy individuals. Thus, differences in clinical
response to IFN-α between HCV-infected AAs and EAs are not
due to an inherent defect(s) in the signal transduction machinery
or the transcriptional regulation of ISGs downstream of IFN-α
signaling, but rather to the way AAs interact with or respond to
HCV and how extensively or rapidly this response changes over
PNAS | January 12, 2010 | vol. 107 | no. 2 | 807
IMMUNOLOGY
Fig. 4. Healthy EAs and AAs do not differ in IFN-α-induced ISG activation.
(A) Summary of the number of genes affected by IFN-α treatment, race, and
interaction between race and treatment as defined by mixed-effects model
ANOVAs applying various combinations of fold change and significance
criteria. (B) Analysis of the impact of race on average ISG response intensity
by displaying trimmed mean (5–5% on both ends) fold change values of
individual ISGs, ranked by average fold change. (C) The impact of IFN-α
treatment on ISG expression in comparison with race and STAT1 phosphorylation by unsupervised hierarchical clustering. Samples are color coded
for treatment (purple, control; pink, IFNa), race (pale blue, European; yellow,
African-American), and STAT1-P FC (pale gray, low STAT1-P FC; medium
gray, medium STAT1-P FC; dark gray, high STAT1-P FC). (D and E) Results of a
similar analysis after reduction of ISG data complexity to three dimensions (x,
y, and z) by multidimensional scaling (MDS). MDS reveals that axis x, representing the principal difference between all samples, is bona fide equivalent with the IFN-α treatment effect, whereas race is virtually irrelevant.
time during the course of infection. The key determinant could
be how racial polymorphisms interact with the virus or virus–
drug interactions rather than with the drug itself. Among possible explanations, both races may react to IFN-α similarly but (i)
HCV suppresses IFN-α signaling in AAs more efficiently than in
EAs, (ii) the immune response to HCV among AAs is somehow
different, creating a difference in the course of the disease that
results in a decreased efficiency of IFN-α, or (iii) patients with
chronic HCV infection represent a population different from
healthy individuals or blood donors, and hence the genetic
background of the one is not representative for the other.
Our findings are in line with the recent observation (15–17) that
in chronic HCV patients, responsiveness to IFN-α therapy is not
affected by SNPs related to IFN-α or IFN-α-stimulated genes, but
strongly influenced by those associated to IFN-λ loci. Likely,
variants of IFN-λ, which is highly expressed in hepatocytes, may
differentially alter the biology of HCV infection in the liver and at
the systemic level, which, in turn, may influence the responsiveness of immune cells to IFN-α stimulation. The finding that IFN-λ
SNPs also affect spontaneous clearance of the virus (18) suggests
that genetic differences can affect disease course from very early
phases of HCV infection independent of therapy, hence indirectly
affecting the outcome of IFN-α therapy.
Recently, others reported that lymphocytes and other circulating mononuclear cells from patients with cancer suffer reduced
phosphorylation of STAT proteins in response to IFN-α (19). This
phenomenon is observable at later stages of cancer progression
(stage II to IV), suggesting that a sufficient bulk of tumor needs to
be present to induce systemic effects. It is, therefore, possible that
a chronic inflammatory process, whether induced by chronic viral
infection or by cancer, may be responsible for the altered innate
immune responses and that AAs may be more susceptible in the
context of HCV infection. Interestingly, these authors observed
that alterations in STAT protein phosphorylation affect most
PBMC subpopulations and they are predominantly pathway specific rather than cell-type specific. Although we did not address all
PBMC populations and focused on the most commonly investigated T cell population, it is likely that these findings could be
generalized on the basis of others’ experience.
1. Reddy KR, et al. (1999) Racial differences in responses to therapy with interferon
in chronic hepatitis C. Consensus Interferon. Study Group. Hepatology 30:
787–793.
2. He XS, et al. (2006) Global transcriptional response to interferon is a determinant of
HCV treatment outcome and is modified by race. Hepatology 44:352–359.
3. Layden-Almer JE, Ribeiro RM, Wiley T, Perelson AS, Layden TJ (2003) Viral dynamics
and response differences in HCV-infected African American and white patients
treated with IFN and ribavirin. Hepatology 37:1343–1350.
4. Muir AJ, Bornstein JD, Killenberg PG, Atlantic Coast Hepatitis Treatment Group (2004)
Peginterferon alfa-2b and ribavirin for the treatment of chronic hepatitis C in blacks
and non-Hispanic whites. N Engl J Med 350:2265–2271.
5. Hepburn MJ, Hepburn LM, Cantu NS, Lapeer MG, Lawitz EJ (2004) Differences in
treatment outcome for hepatitis C among ethnic groups. Am J Med 117:163–168.
6. Su X, et al.; Virahep-C Study Group (2008) Association of single nucleotide
polymorphisms in interferon signaling pathway genes and interferon-stimulated
genes with the response to interferon therapy for chronic hepatitis C. J Hepatol 49:
184–191.
7. Sklan EH, Charuworn P, Pang PS, Glenn JS (2009) Mechanisms of HCV survival in the
host. Nat Rev Gastroenterol Hepatol 6:217–227.
8. Platanias LC (2005) Mechanisms of type-I- and type-II-interferon-mediated signalling.
Nat Rev Immunol 5:375–386.
9. Lesinski GB, et al. (2004) Multiparametric flow cytometric analysis of inter-patient
variation in STAT1 phosphorylation following interferon Alfa immunotherapy. J Natl
Cancer Inst 96:1331–1342.
808 | www.pnas.org/cgi/doi/10.1073/pnas.0913491107
Materials and Methods
Detailed methods are available in SI Materials and Methods.
Blood Samples. Ninety-six consecutively collected blood donations, donated
for research purposes with informed consent, were collected from 78 healthy
EA and AA donors by the Department of Transfusion Medicine, Clinical Center,
National Institutes of Health with Institutional Review Board approval (n = 78,
Table 1).
Sample Processing and Treatments. Whole blood samples were processed by
leukapheresis and Ficoll density gradient centrifugation, and untouched T
cells were isolated by magnetic-bead associated cell sorting, using Miltenyi’s
Human Pan T cell isolation kit for negative selection. T cell purity was
checked by staining CD3+ cells for flow cytometry and found to be 92–95%.
Cells were stimulated with IFN-α 2b (200 IU/mL) for 15 min for flow cytometry or 6 h for gene expression profiling.
Flow Cytometry. Cells were double stained for intracellular STAT1, -2, -3, -4, and
-5 protein levels and STAT1, -2, -3, -4, and -5 phosphorylation after fixation with
paraformaldehyde (PFA) and subsequent methanol permeabilization (see
Table S3 for details). Statistical evaluation was done using Student's t test or
the Mann–Whitney rank sum test to compare means, the chi-square test to
compare the distribution of races between IFN-α response phenotypes, and
Pearson's correlation for correlation studies.
Gene Expression Arrays. Total RNA was processed by a two-cycle amplification
procedure as described elsewhere (20) and hybridized to whole-genome
human 36K oligo arrays, representing 25,100 unique human genes of the
Operon Human Genome Array–Ready Oligo Set version 4.0, printed in house,
using oligos purchased from Operon. ArrayBRB's mixed-effects model ANOVA
was used to identify genes significantly affected by race, IFN-α treatment, or
interaction between race and treatment. Functional gene network analysis
was performed using the Ingenuity pathway analysis system.
Whole-Genome SNP Arrays. Genomic DNA was subjected to array-based SNP
analysis, using Affymetrix’s Genome-Wide Human SNP Nsp/Sty Assay Kit 6.0
per manufacturer's instructions. Data were analyzed using the SNP association analysis module of the Partek GS software package. SNP association to
experimental categories such as race, treatment response, etc., was defined
by performing χ2-tests using the allele association model of Partek GS.
ACKNOWLEDGMENTS. We thank Thomas R. O'Brien, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of
Health, for his useful comments.
10. Rehermann B, Nascimbeni M (2005) Immunology of hepatitis B virus and hepatitis C
virus infection. Nat Rev Immunol 5:215–229.
11. Donnelly RP, Sheikh F, Kotenko SV, Dickensheets H (2004) The expanded family of class
II cytokines that share the IL-10 receptor-2 (IL-10R2) chain. J Leukoc Biol 76:314–321.
12. Chen L, et al. (2009) ISG15, a ubiquitin-like interferon stimulated gene, promotes
hepatitis C virus production in vitro: Implications for chronic infection and response to
treatment. J Gen Virol, in press.
13. MacQuillan GC, et al. (2002) Intrahepatic MxA and PKR protein expression in chronic
hepatitis C virus infection. J Med Virol 68:197–205.
14. Tishkoff SA, et al. (2009) The genetic structure and history of Africans and African
Americans. Science 324:1035–1044.
15. Ge D, et al. (2009) Genetic variation in IL28B predicts hepatitis C treatment-induced
viral clearance. Nature 461:399–401.
16. Suppiah V, et al. (2009) IL28B is associated with response to chronic hepatitis C
interferon-alpha and ribavirin therapy. Nat Genet 41: 1100–1104.
17. Tanaka Y, et al. (2009) Genome-wide association of IL28B with response to pegylated
interferon-alpha and ribavirin therapy for chronic hepatitis C. Nat Genet 41:
1105–1109.
18. Thomas DL, et al. (2009) Genetic variation in IL28B and spontaneous clearance of
hepatitis C virus. Nature 461: 798–801.
19. Critchley-Thorne RJ, et al. (2009) Impaired interferon signaling is a common immune
defect in human cancer. Proc Natl Acad Sci USA 106:9010–9015.
20. Wang E (2005) RNA amplification for successful gene profiling analysis. J Transl Med
3:28.
Pos et al.