Luciferase Assay System
TECHNICAL NOTE:

High-throughput functional analysis of 528 STAT
binding sites in the human genome
Jie Wang1, Katherine Harris2, Troy Whitfield1, Patrick J. Collins2, Shelley Force Aldred2,
Nathan D. Trinklein2, and Zhiping Weng1
1
2

Program in Bioinformatics and Integrative Biology, University of Massachusetts Medical School
Active Motif, Carlsbad, CA

ABSTRACTThe ENCODE consortium has generated binding
maps for the STAT1 and STAT2 transcription factors
during interferon stimulation using the ChIP-seq
method. In this study we wished to functionallycharacterize and map the functional motifs of allof the STAT1 and STAT2 binding sites in the humangenome identified by ChIP-seq. To accomplish thiswe used 528 reporter constructs representing all of
the STAT binding sites. We identified nearly 200 DNA
elements that were inducible by either interferonalpha or gamma. Furthermore we mutagenizednearly 300 5-10 bp motifs to map at high resolution
the functional motifs involved in STAT signaling. The
results show that there are STAT-bound elementsthat respond preferentially to either IFNa or IFNg. The
results also show that the strongest interferon responses
come from proximal promoter regions that are closest
to transcription start sites of genes. Lastly, we usedhigh-throughput site-directed mutagenesis to map thefunctional motifs with base pair resolution.

Figure 1: Model of STAT signaling.
Interferon gamma binds to its receptor and stimulates STAT1
homodimer formation, nuclear translocation, and binding to DNA
elements in the genome. Interferon alpha binds to its receptor
and stimulates STAT1/STAT2 heterodimer formation, nuclear
translocation, and binding to DNA elements in the genome.

INTRODUCTION
To fully understand a genetic regulatory network, it is necessary to map and functionally characterize all of
the DNA regulatory elements involved the network. Experimental approaches exist to map the genome-widebinding sites of transcription factors (i.e. ChIP-seq), however the functional consequences of these binding
events still need to be experimentally determined. In this project, we chose to characterize the STAT-mediatedregulatory network using a comprehensive experimental approach that measures the functional effects of
STAT binding events in the human genome.
The STAT family of transcription factors are important master regulators of the immune response and mediatorsof cytokine signaling. There are 7 different STAT family members in the human genome, and in this study
we focus on STAT1 and STAT2. Different interferons signal through different receptor proteins which in turnactivate STAT proteins by specific phosphorylation events. As shown in Figure 1, in response to interferonalpha (IFNa) stimulation, STAT1 and STAT2 form a heterodimer that translocates to the nucleus and binds to its
genomic targets. Similarly, in response to interferon gamma (IFNg) stimulation, STAT1 forms a homodimer thattranslocates to the nucleus and binds to its genomic targets.

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Luciferase Assay System

To better understand the response of the STAT1/2 regulatory network to interferon stimulation, we used a
combination of bioinformatics and functional genomics approaches as outlined in Figure 2. First, we used
ChIP-seq data generated by the ENCODE consortium to identify sites at which STAT1 or STAT2 is bound in thepresence of IFNa or IFNg. We then used high-throughput cell-based reporter assays and pRT-PCR to determine
which binding events were associated with functional changes in gene regulation. Finally, after identifying
predicted STAT1/2 motifs in the bound and functional elements, we used high-throughput mutagenesis andreporter assays to map the functional motifs with base pair resolution.

METHODS
Initial Screen
The full datasets for STAT1 and STAT2 binding
were downloaded from the ENCODE data
portal at UCSC (genome.ucsc.edu). A total of
528 significant binding sites were identified for
STAT1 and STAT2. Of the 528 total binding sites,
216 were located in proximal promoter regions(<2kb upstream from a TSS in the genome) and
312 were located in distal regions (>2kb from TSS
in the genome). Figure 3 outlines the methods
for using high-throughput reporter assays tofunctionally characterize these binding sites.
For the proximal binding sites, 1- 3 kb promoter
fragments were chosen from the LightSwitch
GoClone Promoter Collection (SwitchGear
Genomics). For the distal binding sites, 0.5-3kb
fragments were cloned into the pLightSwitch
Long Range Element Reporter Vector upstreamof a 200 bp basal TK promoter. All of thepLightSwitch vectors utilize the RenSP luciferase
reporter gene, which is an optimized luciferase
gene designed specifically for use in induction
and repression experiments. These 528 reporter
vectors were then arrayed into a custom 96-wellformat for subsequent assays.

PROJECT GOALS:> Functionally characterize all human STT1 and STAT2 binding sites in
the human genome upon stimulation with IFNa and IFNg
> Determine functional differences between STAT binding at promotersand long-range elements> Identify IFNa and IFNg-specific functional responses> Map the functional motifs for STAT1 and STAT2 for both IFNa andIFNg stimulation

STRATEGY:
Identify 528 STAT1 and STAT2
binding sites in human genome

METHOD:
ENCODE ChIP-Seq Data

Identify and map STAT1 and		 PSSM quantification
STAT2 motifs at bound sites

Identify 178 STAT sites inducible		 High-throughput
by IFNa or IFNg		 reporter assays

Map 247 functional STAT motifs		 High throughput			mutagenesis and reporter			
assaysAs shown in Figure 3, the panel of 528 reporter
vectors were then individually transfected
Figure 2: Project goals and methods.
into K562 cells in 96-well format. A panel of 8
The overall goals of the project are summarized in the figure above. The
promoter controls was also used to normalize
strategies and associated methods are also summarized in the figure.
signals between plates, replicates, andtreatments. Transfection complexes were formed by incubating 100 ng of each individual promoter constructwith Lipofectamine LTX transfection reagent and Opti-MEM media in a total volume of 20 uL and incubatedfor 30 minutes. Transfection complexes were mixed with K562 cells such that 25,000 K562 cells were seeded ina volume of 100 uL in each well of a 96-well white tissue culture treated plate. Six replicate transfection wells
for each promoter construct were performed representing duplicate assays in 3 different conditions: 1) notreatment, 2) 500 U/mL IFNa, and 3) 100 ng/mL IFNg.
After incubating for 16 hours, the transfected cells were treated with IFNa, IFNg, or vehicle only for 8 hours.
Plates were then frozen overnight at -80C.
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Luciferase Assay System

To read the luminescent signal, plates were thawed for 30
minutes at room temperature. Then 100 uL of LightSwitch
Assay Reagent (LS100, SwitchGear Genomics) was added
and incubated for 30 minutes at room temperature.
Then luminescence was read for 2 seconds per well on a
96-well compatible plate luminometer (Molecular Devices
SpectraMax L).
The inducible activity of each promoter was measured
by taking the average treated activity divided by theaverage untreated activity.

qRT-PCR Transcript Level Measurements
K562 cells were grown in 6-well format (1e6 cells/well). A total of 12 wells were treated as follows: 2 wells
received IFNa stimulation (500 U/mL), 2 wells received
IFNg stimulation (100 ng/mL), and 2 wells received notreatment. Total RNA was purified from each well using
the RNeasy Kit (Qiagen). First strand cDNA synthesis
was performed using Superscript III (Invitrogen). qPCR
primers were designed to 96 total transcripts (ampliconsizes ranged from 60-100 bp). qPCR was performed
using iQ SYBR Green Supermix (BioRad) according to
the recommended standard protocol on a BioRad MyIQ
real-time PCR thermal cycler. Relative transcript level
inductions were calculated as: 2^(average thresholdcycle IFN treated – average threshold cycle untreated).

Figure 3: High-throughput reporter assay experimental diagram.
A total of 216 STAT-bound promoter elements and 312 distalelements were cloned into the pLightSwitch Promoter reporter
vector and long-range reporter vector, respectively, as shown
above. These vectors were individually transfected in duplicate
into living cells. Reporter activity was measured after induction
with IFNa, IFNg, and no treatment. The inducible activity ofeach STAT-bound element was calcuated as the ratio of treatedactivity divided by untreated activity.

High-throughput Mutagenesis
The results of the primary functional screen showed that 172 fragments were inducible by either IFNa or IFNg.
The putative STAT1 or STAT2 binding sites in each of these fragments were identified and mutagenized usingsite-directed mutagenesis. Individual motifs were mutagenized by changing 5 bases in a 10 base window,
and all mutant constructs were sequence validated. A total of 247 mutant fragments were generated andfunctionally tested along their wild-type partner using the functional assay described above.

RESULTS & DISCUSSION
One of the fundamental challenges in studying transcriptional networks is ascribing functional consequences
to transcription factor binding events. One transcription factor may bind to hundreds of places in the genome.
Some binding sites are in proximal promoter regions and many binding events are located far away fromgenes. A single transcription factor may also act either as an activator or repressor depending on the target
site, so binding alone does not predict the functional outcome. Furthermore, many binding events have nofunctional effect and may represent evolutionary relics that have no deleterious effects.
With all of these considerations, a direct functional measure of transcription factor binding events is critical tounderstanding transcriptional networks. For this project, we wished to functionally characterize all of the known
STAT1 and STAT2 binding events in the human genome during interferon stimulation. Furthermore, we wishedto see which of these binding sites were associated with IFNa or IFNg-inducible genomic elements. Lastly, weused mutagenesis to map at high resolution the motifs necessary for the interferon response.
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Luciferase Assay System

Using the experimental approach outlined in Figure 3, we
measured the activity of a total of 216 proximal promoter
binding sites (within 2kb of a transcription start site) and
312 distal sites using reporter assays. Of the distal sites, 21%(65/312) showed a 2-fold or greater response to either IFNa
or IFNg. Of the proximal sites, 35% (75/216) showed a 2-foldor greater response to either IFNa or IFNg. Not only is a larger
proportion of the proximal sites inducible, they also are morestrongly inducible. The top 5% of IFNa inducible proximal
sites had a 28-fold average induction upon IFNa treatment,
whereas the top 5% of IFNa inducible distal sites showed
only a 6-fold average induction. Taken together, these datasuggest that on a whole, binding events that are closer totranscription start sites are more likely to be functional andshow stronger activity than binding sites that are locatedfurther away.
We also compared whether some inducible elements weremore inducible by IFNa or IFNg and how this compared to
STAT1 and STAT2 binding status. For the IFNa-inducible proximal
sites, 35 of the fragments showed IFNa-inducible activity
that was at least 2-fold greater that IFNg-inducible activity.
For the IFNg-inducible proximal sites, 11 of the fragments
showed IFNg-inducible activity that was at least 2-fold greaterthat IFNa-inducible activity. Figure 4 summarizes proximal
promoter inducible activities and compares them with
STAT1 and STAT2 binding status. Overall, there is significant
agreement between IFNa inducible activity and STAT1:STAT2
binding under IFNa stimulation, as expected. Likewise, there
is also significant agreement between IFNg inducible activity
and STAT1: STAT1 binding under IFNg stimulation. However,
the heatmap in Figure 4 also highlights the exceptions tothe simple model outlined in Figure 1 and shows how thefunctional activity of these binding sites does not perfectly
correlate to the binding status of the STAT factors.

Figure 4: Promoter activity largely agrees with STAT binding.
In this heatmap, each row is a single STAT-bound proximal
promoter element. The intensity of red in the two lefthand columns indicate the degree of inducible promoter
activity as measured by the reporter assay for IFNa andIFNg induction. The intensity of blue in the columns on theright indicate the ChIP-seq binding signal for IFNa andIFNg induction for STAT1 and STAT2. The promoters weremanually clustered to approximately group the STAT1/1
bound promoters and the STAT1/2 bound promoters.
Overall, the promoters with the strongest binding signal
have the highest inducible activity.

Similarly, Figure 5 illustrates the distal element inducible
activities and how they relate to STAT binding. Like with theproximal elements, there is significant agreement between
IFNa and IFNg inducible activities and STAT1 and STAT2 binding
under IFNa and IFNg induction, respectively. As with theproximal elements, the heatmap shows how the functional
activity of these binding sites does not perfectly correlate to
the binding signal of the STAT factors.

Comparison with endogenous transcript levels
The overall agreement between the ChIP-seq binding
data and the reporter assay functional results provides
strong support for the validity of both of these experimental
approaches. To compliment these two types of data, we also
wished to examine the endogenous transcript levels of STAT
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Luciferase Assay Systemtarget genes to see if this third type of data would explain
any differences between the binding data and reporter
assay results. We used qRT-PCR to measure transcript levelchanges due to IFNa and IFNg stimulation. We sampled a setof 29 genes with STAT binding sites in the proximal promoter
representing a range of inductions and binding scores.
Transcript level induction measurement by qRT-PCR was
performed as described in the methods. The transcript level
induction, promoter activity induction, and STAT binding are
all represented in Figure 6 for the 29 genes tested. Overall,there is a very high correlation between IFNa-induced
promoter activity and transcript levels (R= 0.64). The promoter
activity correlation with transcript level for IFNg stimulation
was even higher (R= 0.72). These results demonstrate thatthe activity of the STAT-bound promoters largely explains thetranscript level changes under interferon stimulation eventhough the promoter region is taken out of its genomic
context in the reporter assay. While changes in transcriptlevels represent the cumulative effect of promoter activity,

Figure 5: Distal element activity agrees with STAT binding.
In this heatmap, each row is a single STAT-bound distalelement. The intensity of red in the two left-hand columns
indicate the degree of inducible activity as measured
by the reporter assay for IFNa and IFNg induction. The
intensity of blue in the columns on the right indicate
the ChIP-seq binding signal for IFNa and IFNg induction
for STAT1 and STAT2. The distal elements were manuallyclustered to group the STAT1/1 bound promoters and theSTAT1/2 bound promoters. Overall, the promoters withthe strongest binding signal have the highest inducible
activity.

Figure 6: Promoter activity correlates with endogenoustranscript levels.
In this heatmap, each row is a STAT-bound promoter. As
in Figures 4 and 5, the intensity of red and blue indicate
reporter activity and STAT binding, respectively. The
intensity of orange in the middle two columns indicate
the endogenous transcript level induction as measured
by qRT-PCR. For the 29 promoters tested, there is a verystrong correlation between induced promoter activity
and induced transcript level indicating that much of the
STAT regulation happens in the proximal promoter region.

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Luciferase Assay Systemlong-range transcriptional elements, and post-transcriptional
mechanisms, these results show that the regulation of STAT
targets are dominated by the proximal promoter region.
As a further test of these observations we also measured
transcript level changes of 30 genes that were locatedclosest to distal STAT binding sites. The comparison of distalelement functional activity with transcript level changes is
considerably more difficult because the association between
distal binding sites and genes is much less certain. Nevertheless,
we made the simple assumption that the closest upstream or
downstream gene was a potential target of a distal binding
site, and there were several occasions where the distal site
was in an intron of a gene. Figure 7 summarizes the relationshipbetween transcript level induction, promoter activity induction,and STAT binding for the sample of 30 distal binding sites. Thereis a much lower correlation between distal element activity
and transcript level induction for both IFNa and IFNg inductions(R= ~0.30). This is likely due to the difficulty in predicting
distal binding site relationships to genes discussed above.
Interestingly, the individual gene labeled 1 highlighted in Figure
7, does show strong agreement, and this distal site occurs in
the intron of the transcript measured by qRT-PCR. This suggests
Figure 7: Distal element activity does not correlate as wellthat intronic STAT binding sites might be more often functionalwith endogenous transcript levels.
In this heatmap, each row is a STAT-bound distal element.
or at least easier to assign to a single gene. Example 2,highlighted in Figure 7, is also interesting. This gene shows strong As in Figures 4 and 5, the intensity of red and blue
indicate reporter activity and STAT binding, respectively.
transcript induction, strong binding, but weak distal element
The intensity of orange in the middle two columns
reporter activity. Interestingly, this gene also has a STAT-boundindicate the endogenous transcript level induction aspromoter that has highly inducible reporter activity. This ismeasured by qRT-PCR. For the 30 distal elements tested,
there is a weaker correlation between induced element
consistent with the previous observation that STAT-induced
activity and induced transcript level.
transcript induction is driven largely by the proximal promoter
region. Lastly, examples 3 and 4 are distal elements withstrongly inducible reporter activity and strong STAT binding but very little transcript induction. These may be cases
where we did not choose the correct target gene, where taking the distal element out of its genomic context
may have removed epigenetic repressors that act in the proper genomic context, or where binding has nofunctional effect. Interestingly many of the highly inducible distal elements are located in SINE repeat elements
indicating that these binding sites may be by products of retro-transposon activity.

High-resolution mapping of functional motifs
The typical resolution for ChIP-seq binding data is usually a hundred bases or more. We wished to map thefunction binding sites within each inducible fragment at a resolution of 10 bases or less. To do this we usedsite-specific mutagenesis to mutate the predicted STAT1 and STAT2 motifs in the inducible fragments that
we experimentally characterized. We generated a total of 247 mutant reporter constructs from a set of 172
wild-type proximal and distal binding sites. For some wild-type constructs we made up to 4 different mutant
constructs to test 4 different motifs. After building and sequence validating the mutant constructs, we thenmeasured the function of each mutant along-side its wild-type partner using the same high-throughput
reporter assay approach described above. We tested each mutant and wild-type partner in triplicate in thesame 3 conditions: 1) no treatment, 2) 500 U/mL IFNa, and 3) 100 ng/mL IFNg.
We tested the inducible activity of each fragment by dividing the average of the treated by the average ofthe non-treated activity. We then calculated the percent change between wild-type inducibility and mutant
inducibility. The vast majority The vast majority of mutants showed significant decreases in inducible activity
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indicating that we were able to map a functional motif in the majority of cases. For example, of the 82 IFNainducible elements that we mutagenized, 76 (93%) showed more than a 50% drop in induction comparedto wildtype and 43 (52%) showed more than a 75% drop in induction level. However, given that significant
inducible activity remained in many of the mutated fragments tested, the mapped functional motifs are
not the only contributors to the inducible activity of the fragment. This supports the notion that regulatory
elements, especially extended promoter regions, are complex functional units and more than just a collectionof independent motifs.
The functional results of our mutation analysis are shown in Figure 8. Mutating either motif in Promoter 1
eliminates virtually all of the inducible activity of the wild-type promoter. In contrast, mutating one of thepredicted motifs in Promoter 2 has no functional effect, whereas mutating the other motif removes all inducible
activity. Lastly, Promoter 3 is an example of motifs that have intermediate effects. One motif eliminates
inducible activity for both IFNa and IFNg. The other motif, however, has no effect on IFNa induction, but
decreases IFNg induction by more than 50%.

CONCLUSIONS
The results of this study demonstrate the value of cell-based
assays that measure the functional effects of transcription
factor binding sites using the STAT signaling pathway as amodel. By using promoter and long-range element reporter
assays, we were able to identify the functional binding sites ina set of over 500 binding sites identified by ChIP-seq. We werealso able to show which of these binding sites were inducible
by IFNa or IFNg, and we were able to provide evidence
that binding sites in close proximity to transcription startsites were more often functional and had stronger effects.
By comparing the reporter assay data with endogenoustranscript levels through qRT-PCR, we were able to showthat a very large proportion of STAT-mediated regulation
happens from the proximal promoter region. Lastly, we usedsite-directed mutagenesis to map the functional motifs withinthese larger regions and dissect the functional behavior ofthese elements in greater detail.

Figure 8: Mutating STAT motifs abolishes inducible activity.
The figure above shows 3 examples of the functional
effects of mutating STAT motifs in proximal promoter
elements. Each promoter is approximately 1 kb in length.
The 3 examples each have 2 predicted STAT motifs thatare indicated as green boxes. The first construct is thewild-type sequence, and the second and third constructs
show each motif mutated separately. The blue and redbars indicate the fold induction of each fragment upon
stimulation with IFNa and IFNg, respectively.

This two-phase approach, 1) TF target discover (ChIP-seq)
followed by 2) functional screening (reporter assays) may
also be generalized to study any transcription factor networkin the human genome. In this way, a comprehensivefunctional profile can be generated for any pathway underthousands of different conditions. This approach would
be very valuable in the pre-clinical development of new
therapeutic compounds that seek to modulate certain
biological pathways. The comprehensive pathway profiles
can quantitatively identify selective modulators of a pathway
and more quickly identify off-target effects.

Supporting on-line materialwww.switchgeargenomics.com/STATproject
For more information on LightSwitch products and services, please visit www.activemotif.com/lightswitch.

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