Genome-wide Profiling of Interleukin-4
and STAT6 Transcription Factor Regulation
of Human Th2 Cell Programming
Laura L. Elo,1,2,13Henna Ja ¨rvenpa ¨a ¨,2,3,13Soile Tuomela,2,3,13Sunil Raghav,2,13Helena Ahlfors,2,4Kirsti Laurila,5
Bhawna Gupta,2Riikka J. Lund,2,6Johanna Tahvanainen,2,7R. David Hawkins,8Matej Ores ˇi? c,9Harri La ¨hdesma ¨ki,5,10
Omid Rasool,2Kanury V. Rao,11,14Tero Aittokallio,1,2,14and Riitta Lahesmaa2,12,*
1Biomathematics Research Group, Department of Mathematics, University of Turku, FI-20014 Turku, Finland
2Turku Centre for Biotechnology, University of Turku and A˚bo Akademi, P.O. Box 123, FI-20521 Turku, Finland
3Turku Graduate School of Biomedical Sciences, Kiinamyllynkatu 13, FI-20520 Turku, Finland
4The National Graduate School in Informational and Structural Biology, A˚bo Akademi University, FI-20520 Turku, Finland
5Department of Signal Processing, Tampere University of Technology, P.O. Box 553, FI-33101 Tampere, Finland
6Department of Biological Science, University of Sheffield, S10 2TN, Sheffield, UK
7Drug Discovery Graduate School, University of Turku, FI-20014 Turku, Finland
8Ludwig Institute for Cancer Research, University of California, San Diego, CA 92037, USA
9VTT Technical Research Centre of Finland, P.O. Box 1000, FI-02044 Espoo, Finland
10Department of Information and Computer Science, Helsinki University of Technology, P.O. Box 5400, FI-02015 TKK, Finland
11International Centre for Genetic Engineering and Biotechnology, P.O. Box 10504, 110067 New Delhi, India
12Immune Disease Institute, Harvard Medical School, Boston, MA 02115, USA
13These authors contributed equally to this work
14These authors contributed equally to this work
Dissecting the molecular mechanisms by which T
helper (Th) cells differentiate to effector Th2 cells is
important for understanding the pathogenesis of
immune-mediated diseases, such as asthma and
allergy. Because the STAT6 transcription factor is
an upstream mediator required for interleukin-4
(IL-4)-induced Th2 cell differentiation, its targets
include genes important for this process. Using
primary human CD4+T cells, and by blocking STAT6
with RNAi, we identified a number of direct and indi-
gration of these data sets with detailed kinetics of
IL-4-driven transcriptional changes showed that
STAT6 was predominantly needed for the activation
of transcription leading to the Th2 cell phenotype.
This integrated genome-wide data on IL-4- and
STAT6-mediated transcription provide a unique
resource for studies on Th cell differentiation and, in
particular, for designing interventions of human Th2
T helper (Th) cells are a subgroup of lymphocytes that are crucial
in the immune system defense against intracellular and extracel-
lular pathogens. The naive Th precursor (Thp) cells are function-
ally immature until activated. After activation, they can differen-
tiate into different subtypes, among which the most studied
are Th1 and Th2 cells. More recently described lineages are
Th17 and regulatory T (Treg) cells (Weaver et al., 2006). Because
the different subtypes have distinct functional roles in the
immune system, disturbances in their balance have been linked
to various immune-mediated diseases. In particular, enhanced
Th1 cell-type responses along with Th17 cell activity are impli-
cated in several autoimmune diseases, such as type 1 diabetes,
whereas inappropriate Th2 cell-type responses might lead to
the development of asthma and atopic disorders (Bettelli et al.,
Th2 cell differentiation is induced by interleukin 4 (IL-4).
Binding of IL-4 to its receptor leads to activation of janus kinase
1 and 3, and phosphorylation of the signal transducer and acti-
vator of transcription protein 6 (STAT6). The phosphorylated
STAT6 forms a homodimer and translocates into the nucleus,
where it binds to specific DNA sequences, thereby regulating
the transcription of its target genes. It is shown that STAT6 is
important for IL-4-driven Th2 cell phenotype in mouse (Kaplan
et al., 1996; Shimoda et al., 1996; Takeda et al., 1996; Zhu
et al., 2001).
Because STAT6 is required as an upstream mediator of IL-4-
receptor-induced Th2 cell differentiation, identification of its
downstream targets is of particular interest. These factors are
likely to include candidates important for eliciting Th2 cell
response. The present study systematically utilized the state-
of-the-art genome-scale measurement technologies together
with efficient computational methods to investigate IL-4-
receptor signaling as a STAT6-centered network that initiates
and dynamically regulates Th2 cell differentiation. STAT6-
regulated genes were identified using RNA interference (RNAi)
technology and the primary target genes of STAT6 were
852 Immunity 32, 852–862, June 25, 2010 ª2010 Elsevier Inc.
dissected from the downstream mediators of the signaling
cascade by chromatin immunoprecipitation followed by deep
sequencing (ChIP-seq). Integrating these data on a comprehen-
sive map of detailed transcriptional kinetics of undisturbed
differentiation of naive human CD4+T cells enabled us to draw
an experimentally validated pathway of molecular events that
mediates IL-4-STAT6 signaling toward Th2 cell phenotype. All
the experimental data have been gathered from human primary
cells, further highlighting the value of the findings and providing
powerful resource of knowledge, for example, for rational design
of therapy for pathogenic human immune responses.
STAT6 in the Center of IL-4-Mediated Transcription
In order to assess the role of STAT6 in the human Th2 cell
differentiation process, we suppressed its expression with
RNA interference and investigated the genome-wide effects of
this perturbation on IL-4-mediated transcriptional regulation.
Samples were collected at 0, 12, 24, 48, and 72 hr time points.
The robustness against technical biases was enhanced by
repeating the experiment separately with three different small
interfering RNAs (siRNA). The decreased protein expression of
STAT6 and its known target GATA binding protein 3 (GATA3)
(Ouyang et al., 1998) verified the efficacy of the RNAi-mediated
knockdown (Figure 1A and Figure S1A available online). In
addition, STAT6 knockdown decreased the number of cells
expressing the human-specific Th2 cell differentiation marker
G protein-coupled receptor 44 (also known as chemoattractant
receptor homologous molecule expressed on T helper type 2
cells, CRTH2) (Cosmi et al., 2000), indicating that the cells could
not acquire the normal Th2 cell phenotype when the expression
of STAT6 was suppressed (Figure 1B).
The effect of STAT6 on the differentiation process was inves-
tigated by identifying those IL-4-regulated genes whose IL-4
effect—either up- or downregulation—changed when STAT6
was knocked down (Table S1). The proportion of IL-4-regulated
genes affected by STAT6-RNAi increased with time, being over
80% after 48 hr of polarization (Figure 1C and Figure S1B). Alto-
gether, the expression of 492 probes representing 453 known
Figure 1. The Overall Effect of STAT6 Knockdown on IL-4-Mediated Regulation during the Human Th2 Cell Differentiation Process
(A) Immunoblotting data showing the expression of STAT6 and GATA3 after introduction of three different STAT6-siRNAs into naive CD4+T cells and culturing for
24 hr in Th2 cell polarizing condition. Nontargeting control-siRNA was used as RNAi control and glyceraldehyde-3-phosphate dehydrogenase (GAPDH) as
a loading control. The representative result from two independent experiments is shown. See also Figure S1A.
(B) The percentage of cells treated with STAT6-siRNA or control-siRNA expressing CRTH2 after 1 week of polarization. The results correspond to the experiment
follows: siRNA #1 456, siRNA #2 742, and siRNA #3 412, and control-siRNA 968.
(C) The percentage of IL-4-regulated genes whose expression was affected by STAT6 knockdown after 12–72 hr of polarization. STAT6 target genes were iden-
tified by determining IL-4 target genes (control-siRNA Th2/Th0, FDR < 0.05) and, subsequently, the genes regulated by STAT6-RNAi (STAT6-siRNA Th2/Th0
compared to control-siRNA Th2/Th0, FDR < 0.05) among the IL-4 targets. Both the IL-4- and the STAT6-regulated genes were identified across three indepen-
dent biological replicates of the time series. See also Experimental Procedures and Figure S1B.
(D and E) Molecular function (D) and cellular localization (E) of the identified STAT6 target genes, according to Ingenuity Pathway Analysis software. See also
Supplemental Experimental Procedures for further explanation of the classifications.
IL-4-Mediated Transcriptome of Human Th Cells
Immunity 32, 852–862, June 25, 2010 ª2010 Elsevier Inc. 853
genes were affected by decreased STAT6 expression. The
broad functional distribution of the identified target genes
(Figure 1D) and their localization throughout all cellular compart-
ments (Figure 1E) reflected the fundamental role of STAT6 in the
global regulation of the Th2 cell phenotype.
Immediate Target Genes of STAT6
To our knowledge, only 6% of the target genes in our RNAi data
are reported to be regulated by STAT6, by using genome-wide
screens performed either on T or B cells of Stat6?/?mice or
STAT6-RNAi experiments, or bound by STAT6 in electrophoretic
bay et al., 1999; Hebenstreit et al., 2003; Kim et al., 2006; Kurata
et al., 1999; Lund et al., 2007; McGaha et al., 2003; Ohmori et al.,
1996; Schaefer et al., 2001; Schro ¨der et al., 2002; Yang et al.,
2005; Zhang et al., 2000; Zhu et al., 2002). In addition, few of
the genes, such as MAOA, are suggested to be regulated by
STAT6 by using indirect methods such as in silico predictions
(Chaitidis et al., 2004). Of the target genes having the largest,
over 2-fold, STAT6 knockdown effects in our study (Figure 2A),
only six, namely GATA3, GIMAP4, IL24, LTB, SOCS1, and
The previously not-reported target genes suggested additional
(Table S1). For example, the downregulation of ST6GAL1 and
RNF125 by STAT6-siRNA linked STAT6 to the determination of
Th2 cell-specific surface glycoprotein structures and ubiquitin
ligase activity, respectively (Toscano et al., 2007; Zhao et al.,
2005).STAT6-siRNA upregulated theexpression ofNCF4,which
is linked to Crohn’s disease (Rioux et al., 2007), rheumatoid
(Matute et al., 2009). MUC1, also upregulated by STAT6-siRNA,
is reported to be induced by IL-12 in human T cells (Agrawal
and Longenecker, 2005), as well as to interact with STAT1 in
showed that genome-wide expression analysis combined with
RNAi can efficiently provide new candidates with potential to
influence the differentiation process and to be further studied
and used for building gene regulatory networks.
Figure 2. STAT6 Mediates IL-4-Induced
Transcription Directly and Indirectly
(A) Heat map presentation of the strongest STAT6
target genes (signal log-ratio between Th2 and
Th0 > 1, and between STAT6-siRNA and control-
siRNA > 1). Genes previously identified as STAT6
targets in genome-wide Stat6?/?mice studies
(Chen et al., 2003; Schro ¨der et al., 2002), RNAi
or EMSA experiments are marked with black
(B) The effect of STAT6 knockdown among the
IL-4-driven target gene expression changes was
calculated using the statistic Th2/Th0 ? sTh2/
sTh0, where Th2/Th0 and sTh2/sTh0 denote the
signal log ratios between the matched Th2 and
Th0 measurements in the control and knockdown
samples, respectively. The histograms present
the average STAT6 knockdown effect for the up-
and downregulated genes in RNAi experiments,
and the error bars represent the standard error of
the mean. The difference in the average STAT6
knockdown effects among the IL-4-upregulated
STAT6 target genes (red bars) and the IL-4-
downregulated target genes (blue bars) is shown
(**Welch two sample t test, p < 0.01).
(C) The proportion of STAT6-regulated genes
identified from the RNAi data (statistically signifi-
cant targets across three independent biological
replicates) that were bound by STAT6 in the
ChIP-seq experiment was determined separately
for the IL-4-up- and IL-4-downregulated genes
(difference between proportions **p < 0.01,
*p < 0.05). The dashed line illustrates the random
expectation as defined by the expected value
of the hypergeometric distribution. See also
Figures S2A and S2B for STAT6 ChIP controls,
Tables S1 and S2 for the STAT6-regulated
and STAT6-bound genes, respectively, and Fig-
ure S2C for the numbers of detections in each
(D) The STAT6 ChIP-seq target genes and the genes which were in addition regulated by STAT6-siRNA were analyzed for the presence of STAT6 binding motifs
(ns, not significant).
(E) Graphical representation of the STAT6 binding sites related to the position of the transcriptional start sites (TSSs). All the ChIP-seq detections were analyzed
using the Genomatix RegionMiner tool (http://www.genomatix.de, Genomatix Software GmbH). TSS is defined to be at position zero in the graph.
IL-4-Mediated Transcriptome of Human Th Cells
854 Immunity 32, 852–862, June 25, 2010 ª2010 Elsevier Inc.
The RNAi experiments demonstrated that the effect of the
STAT6 knockdown was, in general, significantly larger among
the IL-4-upregulated than among the IL-4-downregulated genes
(Figure 2B). This suggested that STAT6 primarily drives the acti-
vation of transcription, whereas downregulation would mainly
remain a downstream effect after IL-4 stimulation. To distinguish
the direct STAT6 targets from the secondary effects of STAT6,
we exploited ChIP-sequencing (ChIP-seq) (Johnson et al.,
2007; Robertson et al., 2007). Because the level of phosphory-
lated STAT6 (Tyr641) reached its maximal level readily after
IL-4 stimulation in our experimental setup (Figure S2A) and
because STAT6 is shown to bind to its targets quickly after the
IL-4 stimulus in previously published data (Andrews et al.,
2002), the binding was measured within 4 hr after initiation of
polarization. The success of the STAT6 ChIP experiment used
for sequencing was validated with detection of STAT6 binding
to the promoter of its known target gene SOCS1 (Figure S2B)
(Hebenstreit et al., 2006).
In total, 508 genes were bound by STAT6 IL-4-dependently
within 10 kb of the transcription start or end site (Table S2).
Twenty percent to nearly 30% of the upregulated target genes
observed with STAT6-RNAi across the time points analyzed
were confirmed to be direct targets of STAT6 on the basis of
the ChIP-seq results (Figure 2C and Figure S2C). Among the
genes downregulated by STAT6, the proportion of direct
STAT6 targets was significantly lower, less than 10%, than
among the upregulated genes. These observations suggested
that most of the downregulated target genes were not direct
targets of STAT6, but the regulation of their expression depends
on secondary regulatory factors.
Seventy-nine percent of the identified STAT6 binding
sites contained STAT family sequence motif (Table S3). The
closer investigation of the known STAT6 consensus motifs
(Hebenstreit et al., 2006) (Figure 2D) revealed that TTCN3GAA
and TTCN4GAA motifs were the ones that were most commonly
found, 23% and 50% respectively, within the ChIP-seq peaks.
Interestingly, when comparing the frequency of the motifs
between the STAT6-regulated genes and all the STAT6-bound
genes, we found a statistically significant increase in the occur-
lated genes (p < 0.05).
Of the detected STAT6 binding sites, 66% resided in the intra-
genic regions and 34% were located either upstream of the tran-
assessed using the Genomatix RegionMiner tool (http://www.
majority of the identified intragenic STAT6 binding sites were
located in the introns of the genes, the first two introns being
the most common. Further investigation of the detected STAT6
binding sites along the target genes revealed that there is a clear
enrichment of the peaks in close proximity of transcription start
site (Figure 2E) as noticed with other STAT factors (Kwon et al.,
2009; Robertson et al., 2007). Almost 10% of the identified
STAT6 binding sites were more than 100 kb away from any
known gene based on the CisGenome annotation software
(Table S3) (Ji et al., 2008), which most probably reflects the over-
hits may indicate that there are still novel transcripts to be found
close by these binding sites or that these sites are used as reser-
voir of STAT6 molecules. An intriguing option is also that these
sites are needed for regulation of the genes located far away
when measured directly by the distance along the DNA strand
but which are brought together by the regulation of looping of
Transcriptional Regulation by IL-4
Because our ultimate goal was to understand in detail how IL-4
drives the Th2 cell differentiation, we constructed a more
comprehensive kinetic profile of the genome-wide transcrip-
combined with T cell receptor (TCR) activation. The early differ-
entiation process at nine time points between 0.5 and 72 hr after
polarization was studied (Figure 3A). In total, 640 genes were
upregulated and 460 genes were downregulated by IL-4 at one
or more time points (Table S4). Clustering of the data demon-
strated that IL-4-specific signaling was initiated with transient
changes in gene expression followed by the stable Th2 cell
studies (Ha ¨ma ¨la ¨inen et al., 2001; Lund et al., 2007; Nagai et al.,
2001; Rogge et al., 2000), our results revealed that IL-4-induced
regulation of transcription in human cells was highly dynamic.
The overall expression kinetics revealed that the early IL-4-
mediated signaling can be roughly split into two phases: the
rapid wave of upregulation from 0.5 to 4 hr is followed by down-
regulation starting at 6 hr after polarization (Figure 3A). The upre-
gulated genes were significantly enriched with direct STAT6
targets already at 0.5 hr, whereas the enrichment among the
downregulated genes was detected only at later time points
and at weaker significance levels. Overlay of the STAT6 ChIP-
seqhitstotheIL-4target geneswithin thefirst4hrofpolarization
displayed the group of genes directly downstream of STAT6
likely to be responsible for mediating the effects of IL-4. The
genes selected based on signal log-ratio (Th2/Th0 > 1) are pre-
sented in Figure 3C.
The fact that STAT6 also had direct target genes which are
regulated only at the later time points, speaks for the importance
of cofactors and putatively also epigenetic changes determining
the role of STAT6. For example, of the known STAT6 coactiva-
tors (Goenka and Boothby, 2006; Hebenstreit et al., 2006),
NCOA3 was upregulated peaking at 4 hr, whereas PARP14
was downregulated from 24 hr onward (Table S4). Moreover,
the presence of competing transcription factors that can recog-
nize similar binding sites as STAT6, such as BCL6 (Hebenstreit
et al., 2006), can influence the availability of DNA binding sites.
In our data, the expression of BCL6 and its corepressor BCOR
were downregulated by IL-4, supporting the importance of
turning down of this transcription factor, possibly competing
with STAT6 DNA binding during the Th2 cell differentiation.
Further investigation of the kinetics of STAT6 binding to its
primary target genes identified in this study (Figure S3) sug-
gested that STAT6 occupancy at its target sites may change
during the differentiation process (Figure 4). This again rein-
forced the notion that human Th2 cell differentiation is a strictly
regulated step-wise process.
To detect potential secondary factors needed for enhance-
ment of Th2 cell polarization, we identified STAT6-regulated
genes that were not among our ChIP-seq hits, i.e., indirect
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STAT6 target genes. We investigated these genes for the pres-
ence of enriched transcription factor binding motifs based on
the PreMod database (Table S6), focusing on high-scoring sites
in modules located within 10 kb upstream of the transcription
start sites (Blanchette et al., 2006). Notably, STAT6 binding motif
could not be found enriched among the indirectly regulated
genes, further indicating thesegenesto besecondary or atypical
targets of STAT6. Instead, we found STAT5A homotetramer
motif to be among the most significantly enriched motifs
(Table S6). STAT5 is known to play an important role in Th2
cell polarization (Zhu et al., 2006). As the binding of this factor
is studied with ChIP-seq in mouse CD4+T cells at 8 and 13 hr
after IL-4 stimulation (Liao et al., 2008), we compared the identi-
fied STAT5A target genes to the STAT6 secondary target genes
identified in the present study. The statistically significant over-
lap found supports the idea that STAT5A may be a downstream
regulator of STAT6 target genes. Furthermore, by comparing
the STAT6-regulated ChIP-seq target genes to the reported
Figure 3. Dynamics of IL-4-Mediated Gene Expression
(A) The number of IL-4-regulated genes identified from the transcriptomics experiments is shown at each time point (see Table S4). The height of a bar shows the
total number of detections at a particular time point, while the height of the darker inner part represents those genes that have not been detected at earlier time
points. Enriched STAT6 binding, on the basis of the ChIP-seq experiment, is indicated with stars above the bars (hypergeometric test **p < 0.01 or *p < 0.05),
referring tothegenes detected tobedifferentially expressed forthe firsttime ataspecific time point. TheChIP-seq peaks werelinked tothe geneswithmaximum
distance of 10 kb up- or downstream from the transcribed regions.
(B) Transcription profiles of the differentially expressed genes between the Th2 and Th0 conditions. The genes were clustered using the hopach algorithm
(Bioconductor hopach package) with cosine angle as a dissimilarity measure. See also Table S5.
(C) Heatmap of selected IL-4-regulated genes (signal log-ratio between Th2 and Th0 > 1) within 4 hr of Th2 polarization found to be bound by STAT6. Genes
regulated by STAT6 in RNAi experiments are marked with black circles. STAT6 ChIP-seq targets were identified based on one experiment. IL-4-mediated tran-
scriptomics and RNAi data represent statistically significant changes over three independent experiments.
IL-4-Mediated Transcriptome of Human Th Cells
856 Immunity 32, 852–862, June 25, 2010 ª2010 Elsevier Inc.
STAT5A target genes (Liao et al., 2008), we found a significant
overlap between these data sets, further proposing that STAT6
and STAT5A might regulate even the same genes (Table S6).
Core Transcription Factor Interactions
Categorization of the IL-4-controlled and STAT6-regulated
genes into functional groups along the time points (Table S7)
supported the hypothesis that Th2 cell polarization is initiated
by fast regulation of transcription factors promoting phenotypic
differentiation (Figure 5A and Figure S4). The activation of
transcription factors followed the same kinetics as the overall
IL-4-dependent signaling (Figure 3A), emphasizing its role in
determining the timing of the differentiation process. Based on
the results from complementary approaches used in this study,
we categorized the mediators of the IL-4-specific transcriptional
program into four groups of transcription factors: (1) STAT6-
independent, (2) putative STAT6 targets, (3) STAT6-dependent
primary targets, and (4) STAT6-dependent secondary targets.
The selected members of each category are presented in
In the mouse system, the expression of Xbp1 and Ncoa3 is
regulated via STAT6 in B cells (Schro ¨der et al., 2002), but in
our data, both of these genes were STAT6 independent.
tion factorsinducedbyIL-4within the firsthours afterstimulation
(Table S4). XBP1 regulates the unfolded protein response (UPR)
needed for increased expression of secreted and membrane
proteins, and it is required for maturation of plasma cells (Bruns-
ing et al., 2008). UPR is active in immature CD4+CD8+double
positive thymocytes, but inactive in spleen CD4+T cells (Bruns-
ing et al., 2008). This suggested that XBP1 can have a role inde-
pendent of UPR in polarization of Th cells or that IL-4 stimulation
reactivates this pathway. LRRFIP1, in turn, which was also
among the earliest transcription factors induced by IL-4, was
identified as a putative STAT6 target bound by STAT6 in the
ChIP-seq experiment, but not detected as STAT6-regulated in
factor expression (Suriano et al., 2005) and an enhancer of
b-catenin mediated transcription (Lee and Stallcup, 2006).
We identified three transcription factors, BATF, EPAS1, and
RUNX1, to be directly regulated by STAT6. RUNX1 is previously
linked to inhibition of Th2 cell polarization via downregulation of
GATA3 expression (Komine et al., 2003) and through binding
to the IL-4 silencer (Naoe et al., 2007). RUNX1 can also form
a complex with FOXP3 and RORC and is indispensable for Treg
and Th17 cell function, respectively (Kitoh et al., 2009; Zhang
et al., 2008). Due to the general importance of RUNX proteins in
different Th cell subtypes, they are suggested to function as
core modifiers of effector CD4+T cell functions (Kitoh et al.,
2009). Interestingly, EPAS1 binds to the promoter of RUNX1
(Mole et al., 2009) and may amplify STAT6 effect. BATF, the third
transcription factor found to be directly regulated by STAT6, is
shown to be needed for Th17 cell differentiation (Schraml et al.,
2009), and more recently, for Th2 cell development as well
(Betz et al., 2010). These STAT6-dependent primary targets are
putative key initiators of IL-4-induced transcriptional program.
Intriguingly, GATA3 could not be recognized as a primary
STAT6 target gene in our ChIP-seq analysis, although GATA3
transcription was regulated by IL-4 shortly after initiation of
polarization. This can indicate that there is still, in addition to
STAT6, room for other regulators of early GATA3 expression or
that STAT6 regulates the expression of GATA3 via distant regu-
Interestingly, BATF, one of the direct STAT6-dependent tran-
scription factors, is identified to regulate Gata3 transcription
(Betz et al., 2010). STAT6 also indirectly upregulated the expres-
sion of GFI1 and NFIL3, which are linked to regulation of GATA3
protein stability (Shinnakasu et al., 2008) and natural killer cell
development (Gascoyne et al., 2009; Kamizono et al., 2009),
respectively. BHLHE40, ID3, IRF8, and STAT1 were indirectly
downregulated by STAT6, suggesting that the expression of
these factors is disadvantageous for Th2 cell differentiation.
In addition to their putative individual role in the differentiation
process, the identified IL-4- and STAT6-regulated transcription
factors formed a compact core interaction network (Figure 5C).
This suggested that Th cell commitment is defined by combina-
torial signaling pathways, acting together to determine the func-
tional outcome. The core network also illustratively revealed that
Figure 4. Kinetics of STAT6 Occupancy on Its Target Genes
of input value is an average of two to three cultures showing enrichment, with the bars representing the corresponding standard error. The primers and probes
used are listed in Supplemental Experimental Procedures. See also Figure S3.
IL-4-Mediated Transcriptome of Human Th Cells
Immunity 32, 852–862, June 25, 2010 ª2010 Elsevier Inc. 857
there was a close relationship between genes engaged in deter-
mining the different Th cell fates, as all known STAT family
members could be connected to each other via a relatively
limited number of molecules.
Despite the fact that STAT6 plays a fundamental role in the
processes associated with the Th2 cell branch of the immune
Figure 5. IL-4-Mediated Dynamic Network of Tran-
(A) IL-4 triggers the Th2 cell-specific transcriptional program
via regulation of transcription factors. The height of a bar
while the height of the darker inner part represents those tran-
scription factors that have not been detected at any earlier
time point. The expression profiles of these transcriptional
regulators are listed in Table S8. See also Figure S4.
(B) IL-4 signal transmission through STAT6-dependent and
and induction, when compared to Thp sample (signal log ratio
Th2/Thp > 1). Although the STAT6-independent genes were
not regulated by STAT6-siRNA, they were induced by IL-4 in
the control-siRNA experiments (FDR < 0.05). STAT6-indepen-
dent genes were also required to have at least 2-fold expres-
sion difference between Th2 and Th0 sample. Putative STAT6
target gene LRRFIP1 was induced by IL-4 in our kinetic data
(signal log-ratio Th2/Thp and Th2/Th0 > 1) and bound by
STAT6 in ChIP-seq experiment but was not regulated by
STAT6 in the RNAi experiments. The average signal log-ratios
betweentheTh2 and Th0conditions from 0.5 to72hrare visu-
alized as bar charts next to the nodes.
(C) IL-4- and STAT6-regulated transcription factors formed
a complex core network of interacting nodes. The core tran-
scription factor network represented in Figure 5B (inner circle)
was further expanded with known transcription factor interac-
tions (outer circle). STAT6-mediated regulation detected in
this study is marked with red edges (solid edge for directregu-
actions between the putative downstream transcriptional
regulators in human were added to the figure. Blue edges
correspond to protein-protein interactions, and black ones
correspond to other type of interaction or regulation. The
networks were generated through the use of Ingenuity Path-
ways Analysis (Ingenuity Systems, www.ingenuity.com). The
data presented shows the statistically significant changes
over three independent experiments.
system, its target genes in human are not known.
To get a more systematic knowledge about the
mechanisms involved, we identified STAT6 target
genes during the early stages of human Th2
cell differentiation process. We showed that
STAT6 resided in the core of IL-4-driven transcrip-
tional events in human CD4+T cells. Importantly,
a marked association was observed between the
STAT6-upregulated genes and its direct targets,
indicating that after IL-4 stimulation, STAT6 was
primarily needed for the activation of pathways
leading to the Th2 cell phenotype. In our data,
only a subset of the genes differentially regulated
throughout the analyzed time frame. This indicated that there
are both switch kind of genes needed at specific time point
and factors that are important both for the transition to the
new developmental pathway and for maintaining the already
acquired phenotype. Detailed dissection of the functional role
of these downstream factors may require analysis within a very
specified timeline, as well as modulation of combinations of
IL-4-Mediated Transcriptome of Human Th Cells
858 Immunity 32, 852–862, June 25, 2010 ª2010 Elsevier Inc.
As reported in other previous genome-wide ChIP experiments
with other transcription factors, only a relatively small fraction
(?30%) of the STAT6-bound genes were evidently associated
with gene expression changes (Massie and Mills, 2008). One
plausible cause for the DNA binding, which is not connected to
gene regulation, is that the loci might not permit the recruitment
of cofactors that are required to modulate the transcriptional
activity. In contrast, STAT6 may also regulate its target genes
via long-distance interactions, for example, via looping of the
chromatin, and these interactions cannot be identified computa-
tionally. Previously STAT6 has been shown to regulate the intra-
chromosomal interactions of Th2 cell cytokine locus, which is
suggested to coordinate cytokine expression in the effector cells
(Spilianakis and Flavell, 2004). In addition to the transcription
regulation mediated directly via DNA binding, STAT6 may regu-
(Ohmoriand Hamilton, 2000)or bymodulating epigenetic marks,
such as posttranslational modifications of histones or DNA
methylation. Furthermore, a possibility that is completely unex-
plored on a genome-wide scale is that the role of STAT6 may
change during the commitment and effector phase of Th2 cell
lineage. Our kinetic ChIP-PCR data, although with a limited
target gene set, already indicated that STAT6 occupancy on its
target genes varies during time.
The present study provides a unique source of IL-4-STAT6-
mediated regulation of gene expression during the early human
Th2 cell differentiation process. Interpreting the initial steps
of this process is also crucial for understanding the Th cell polar-
ization in general because our results underlined the close
connection between alternative Th cell phenotypes. STAT1
and STAT4 regulating Th1 cell differentiation, STAT3 acting on
Th17 cell subtype, and STAT5 and STAT6 needed for Treg
and Th2 cell development (Adamson et al., 2009), respectively,
could all be connected to each other. This highlighted the
importance of gathering detailed information of the role of all
STAT family members, in order to thoroughly understand the
complexity of determination of Th cell phenotype. In mouse,
the STAT5A is shown to be responsible for STAT6 independent
Th2 cell commitment, and Stat6?/?Stat5a?/?mice have mark-
edly reduced airway inflammation compared to Stat6?/?(Taka-
tori et al., 2005). The overlap of our STAT6 results to previously
published STAT5A data (Liao et al., 2008) suggests that the
co-operational role of these factors should be further studied in
Our data set illustrated the diverse effects of STAT6, from the
level of DNA binding to the control of transcription during the first
steps of Th2 cell polarization. The data presented in this study
provides a solid basis for subsequent research on the role of
STAT6 in the committed and effector Th2 cells. In addition, the
data presented allow more detailed modeling of the role of
STAT6 in the initiation of Th2 cell differentiation process. While
genome-wide systems are beyond the reach of detailed predic-
tive models, selected subnetworks could be used as starting
points, for instance, for differential equation models of specific
subsystems. Undoubtedly, our data of STAT6-mediated tran-
scriptional control alone and combined to the genome-wide
datasets acquired with other STAT molecules (Liao et al., 2008;
Robertson et al., 2007) provide important insight to the steps
needed for developing and regulating Th cell responses. Impor-
tantly, the findings on primary human cells, such as those pre-
sented in this study, are of particular value providing resource
for new avenues for tackling the harmful immune reactions.
Human CD4+T Cell Purification and Culturing
Umbilical cord blood was collected from healthy neonates born in Turku
University Hospital, Hospital District of Southwest Finland. Mononuclear cells
were isolated (Ficoll-Paque, Amersham Biosciences), after which CD4+T cells
were collected (Dynal CD4 Positive Isolation Kit, Invitrogen). Cells were
activated with plate-bound aCD3 (500 ng/24-well culture plate well, Immuno-
tech, France) and soluble aCD28 (500 ng/ml, Immunotech) in density of 2 to
4 3 106cells/ml of Yssel’s medium (Yssel et al., 1984) containing 1% human
AB serum (PAA). Th2 cell polarization was initiated with IL-4 (10 ng/ml, with
or without neutralizing aIL-12 10 mg/ml, both R&D Systems). Cells activated
without differentiating cytokines were also cultured (Th0). At 48 hr, IL-2 was
added to the cultures (17 ng/ml, R&D Systems).
RNAi-Mediated STAT6 Knockdown
STAT6-siRNAs #1 (50-AAGCAGGAAGAACTGAAGTTT-30), #2 (50- GAATCAGT
CAACGTGTTGTCA-30),or #3(50-CAGTTCCGCCACTTGCCAAT-30),or nontar-
geting control-siRNA (50-GCGCGCTTTGTAGGATTCG-30) were introduced to
the cells (Sigma,1.5 mg/4 3 106cells) with Nucleofector (Amaxa Biosystems),
after which cells were rested 24 hr before culturing. For STAT6 target gene
was repeated three times, each time with different siRNA targeting STAT6.
Total RNA (RNeasy Mini Kit, QIAGEN) was processed and hybridized on
Illumina BeadChip Human-6 v2 arrays (Illumina Inc., San Diego, USA). All the
microarray samples included in this study have been prepared at the Finnish
Microarray and Sequencing Centre, Turku, Finland. CRTH2-PE staining (no.
130-091-238, Miltenyi Biotech) was performed after 1 week of polarization
and analyzed with LSR II flow cytometer (BD Biosciences) and Cyflogic soft-
ware (CyFlo Ltd, Finland). Western detections were probed with the following
antibodies: STAT6 (no. 611291, BD Biosciences), GATA3 (no. 558686, BD
PharMingen), and GAPDH (no. 5G4, MAb 6C5, HyTest).
Identification of IL-4-STAT6 Targets
The microarray data were quantile-normalized (Bioconductor affy package)
and log2-transformed in each experiment. IL-4-regulated genes were identi-
fied between the matched Th2- and Th0-measurements (same time point
and culture) in the control-siRNA data using linear modeling with moderated
F- and t-statistics (Bioconductor limma package). Genes with false discovery
rate (FDR) < 0.05 in the overall F-test and further at least in one of the t tests for
the individual time points were defined as changed (Benjamini and Hochberg,
1995). The effect of STAT6 knockdown on the IL-4-regulated genes was
then assessed using the statistic Th2/Th0 - sTh2/sTh0, where Th2/Th0 and
sTh2/sTh0 denote the signal log-ratios between the matched Th2- and Th0-
measurements in the control and knockdown data, respectively. Consistent
IL-4-STAT6 regulation across the biological repeats was identified using the
moderated F- and t-statistics at FDR < 0.05, similarly as above.
STAT6 ChIP-seq Studies
CD4+T cells were cultured in Th0 or Th2 cell polarizing condition for 1 and 4 hr,
and naive cells were used as a control. ChIP was performed as described
previously (Li et al., 2003). The cells were sonicated using Bioruptor sonicator
chromatin was incubated with 10 mg of STAT6 antibody (M-20, Santa Cruz
Biotechnology, Inc.) coupled to the magnetic beads (no. 112.04 Dynal
Biotech). The crosslinks were reversed (65?C for 12 hr), and DNA was treated
sequentially with Proteinase K and RNase A and purified (QIAquick PCR puri-
fication kit, QIAGEN). The library preparation was performed according to the
Illumina recommendations (Fasteris Life Sciences, Switzerland). Sequencing
was performed on Illumina Genome Analyzer GAII producing from 4 to
5.2 million reads per sample. The reads were aligned to the human reference
genome (NCBI v36) using SOAP software (Li et al., 2008). Only uniquely map-
ped reads were retained (?3 million reads per sample). Potential binding
IL-4-Mediated Transcriptome of Human Th Cells
Immunity 32, 852–862, June 25, 2010 ª2010 Elsevier Inc. 859
the FindPeaks software (version 220.127.116.11) following the recommendations in the
FindPeaks manual (Fejes et al., 2008), which showed, on average, the best
peak detection performance in this particular data set (Laajala et al., 2009).
To further remove potential false positives because of nonuniform back-
ground, a minimum of 10-fold enrichment of reads in the Th2 sample relative
to the corresponding location in the Thp sample was required. Moreover, we
focused only on the Th2 cell-specific peaks that additionally showed at least
a 2-fold read enrichment in the Th2 sample relative to the corresponding
Kinetic qPCR Analysis of Selected ChIP-seq Targets
We selected 10 ChIP-seq STAT6 binding regions for kinetic analysis of STAT6
binding at 0, 4, 12, and 72 hr. ChIP followed by qPCR was performed using
either FAM and TAMRA labeled probes (negative controls GATA3 and IFNg)
or Universal ProbeLibrary probes (Roche Applied Science) with custom
ordered oligos (Supplemental Experimental Procedures; STAT6 ChIP-seq
section) designed with Universal ProbeLibrary Assay Design Centre (Roche),
in Absolute QPCR ROX Mix (Thermo Scientific). The qPCR runs were analyzed
with 7900HT Fast Real-Time PCR System (Applied Biosystems). Percent of
input values were calculated with the following equation: 100 3 2(Input ? Ct
[ChIP]), where input was adjusted to 100%.
Transcriptional Profiling of IL-4 Targets
Samples were collected at 0, 0.5, 1, 2, 4, 6, 12, 24, 48, and 72 hr time points.
RNA (RNeasy Mini Kit, QIAGEN) from three cultures was processed and
hybridized on Affymetrix GeneChip HG-U133 Plus 2.0 arrays (Affymetrix,
Santa Clara, USA). Of the 54 hybridizations, two were excluded from further
data analysis based on the compromised quality of the samples (Th0 4 hr
and Th2 6 hr). The microarray data were quantile-normalized (Bioconductor
affy package) and log2-transformed. IL-4-regulated genes were identified
between the matched Th2 and Th0 measurements (same time point and
culture) using the probe-level expression change averaging procedure PECA
(Elo et al., 2005) together with linear modeling (Bioconductor limma package).
The probe-level estimates of the moderated F and t statistics were summa-
rized into probeset-level values using the Tukey biweight average, and the
significance of an expression change was determined based on the analytical
p value of the estimated probeset-level statistic. Probesets with p < 0.05 in the
overall F-test and further at least in one of the t tests for the individual time
points were defined as changed.
The usage of blood of unknown donors was approved by the Ethics
Committee of the Hospital District of Southwest Finland.
The data discussed in this publication are accessible through GEO
SuperSeries accession number GSE18017 (http://www.ncbi.nlm.nih.gov/
Supplemental Information includes four figures, eight tables, Supplemental
Experimental Procedures and can be found with this article online at doi:10.
We thank the technical personnel for assistance; the staff of the Finnish Micro-
array and Sequencing Centre, Turku, Finland for microarray hybridizations; all
Finland) for the sample collection; and A. Rao for the critical reading of the
manuscript. The work was supported by the Academy of Finland (grants
77773, 203725, 207490, 116639, 115939, 123864, 126063, 127575 [L.L.E.],
and 120569 [T.A.]), the European Commission Seventh Framework grants
(EC-FP7-SYBILLA-201106, EC-FP7-NANOMMUNE-214281 and EC-FP7-
DIABIMMUNE-202063), CIMO/Sitra grant (S.R. and R.L.), The Sigrid Juse ´lius
Foundation (R.L. and K.V.R.), The Department of Biotechnology, Government
of India (K.V.R), and Turku University Hospital Grant.
Received: December 11, 2009
Revised: April 20, 2010
Accepted: May 26, 2010
Published: June 24, 2010
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862 Immunity 32, 852–862, June 25, 2010 ª2010 Elsevier Inc.