Genetic framework for GATA factor function in
Amelia K. Linnemanna, Henriette O’Geenb, Sunduz Kelesc, Peggy J. Farnhamd, and Emery H. Bresnicka,1
aDepartment of Cell and RegenerativeBiology, WisconsinInstitutes for Medical Research, University of WisconsinCarbone CancerCenter, University of Wisconsin
School of Medicine and Public Health, Madison, WI 53705;bGenome Center, University of California, Davis, CA 95616;cDepartment of Statistics and Biostatistics
and Medical Informatics, University of Wisconsin School of Medicine and Public Health, Madison, WI 53706; anddUniversity of Southern California/Norris
Comprehensive Cancer Center, Los Angeles, CA 90089
Edited by Stuart H. Orkin, Children’s Hospital and the Dana Farber Cancer Institute, Harvard Medical School, and Howard Hughes Medical Institute, Boston,
MA, and approved June 28, 2011 (received for review May 26, 2011)
Vascular endothelial dysfunction underlies the genesis and pro-
gression of numerous diseases. Although the GATA transcription
factor GATA-2 is expressed in endothelial cells and is implicated in
coronary heart disease, it has been studied predominantly as
a master regulator of hematopoiesis. Because many questions re-
garding GATA-2 function in the vascular biology realm remain
unanswered, we used ChIP sequencing and loss-of-function strat-
egies to define the GATA-2–instigated genetic network in human
endothelial cells. In contrast to erythroid cells, GATA-2 occupied
a unique target gene ensemble consisting of genes encoding key
determinants of endothelial cell identity and inflammation. GATA-
2–occupied sites characteristically contained motifs that bind acti-
vator protein-1 (AP-1), a pivotal regulator of inflammatory genes.
GATA-2 frequently occupied the same chromatin sites as c-JUN and
c-FOS, heterodimeric components of AP-1. Although all three com-
ponents were required for maximal AP-1 target gene expression,
GATA-2 was not required for AP-1 chromatin occupancy. GATA-2
conferred maximal phosphorylation of chromatin-bound c-JUN at
Ser-73, which stimulates AP-1–dependent transactivation, in a
chromosomal context-dependent manner. This work establishes a
link between a GATA factor and inflammatory genes, mechanistic
insights underlying GATA-2–AP-1 cooperativity and a rigorous ge-
netic framework for understanding GATA-2 function in normal
and pathophysiological vascular states.
pathophysiology of the vascular system involves dissecting
transcriptional mechanisms underlying the development and
function of endothelial cells. Transcription factors implicated in
endothelial transcriptional mechanisms (1) include GATA bind-
ing protein 2 (GATA-2), which is required to generate and/or
maintain multipotent hematopoietic precursors during embryo-
genesis (2, 3). GATA transcription factors function as master
regulators of development and have important functions in dif-
cells (5, 6), the GATA-2–driven genetic network in endothelium
and its role in vascular biology are unclear.
The pathophysiological importance of GATA-2 in the vascular
system is supported by the finding that GATA2 polymorphisms
correlate with coronary artery disease (7). GATA-2 regulates
genes that encode important mediators of endothelial cell func-
tion, including angiopoietin-2 (8), matrix metalloproteinase-2 (9),
and vascular cell adhesion molecule-1 (10). In vitro and in vivo
studies indicate that GATA-2 functions in human umbilical vein
endothelial cells (HUVEC) and retinal endothelial cells to medi-
ate mechano-signaling–dependent angiogenesis, which involves
GATA-2–dependent induction ofVEGF receptorII(11).Despite
this compelling evidence, Gata2-null mice die at embryonic day
10.5 (E10.5) because of severe anemia, although their vasculature
appears to be morphologically normal (3). However, the absence
of an overt vasculogenesis defect does not negate the importance
of GATA-2 in adult endothelium.
n important approach to understanding the biology and
We used ChIP sequencing (ChIP-seq) and expression profiling
to describe a GATA-1– and GATA-2–driven genetic network in
human K562 erythroleukemia cells, which resemble primitive
erythroid cells (12). This dataset, which was validated in primary
erythroid cells (12), lacked genes that impart a unique identity to
endothelial cells. We hypothesize that the GATA-2 target gene
ensembles in endothelial and hematopoietic cells differ consider-
ably,but analogoustoitshematopoieticfunctions, GATA-2exerts
important activities in endothelium.
Herein we use ChIP-seq and expression profiling in HUVEC to
define a GATA-2–driven genetic network that differs greatly from
that of K562 cells. The composition of the network indicated that
GATA-2 functions in concert with activator protein-1 (AP-1), a
pivotal regulator of inflammation (13, 14), thus linking GATA-2
with inflammatory processes. Given the dearth of mechanistic in-
formation regarding vascular functions of GATA-2 and the broad
scope of pathophysiologies involving vascular dysfunction with
associated inflammation (15), linking endothelial GATA-2 to in-
flammatory genes establishes an important framework for un-
derstanding the role of GATA-2 in normal and pathological
Results and Discussion
Exquisite Cell Type-Specificity of GATA-2 Chromatin Occupancy. To
establish a framework for understanding GATA-2 function in
endothelium, we used ChIP-seq to define the GATA-2 chromatin
target sites in HUVEC. The ChIP assay was validated by quan-
which activates a linked LacZ reporter in the vasculature of
transgenic mouse embryos (17). GATA-2 occupancy at the +9.5
kbsite was ∼15-fold greater thanthepreimmune control, whereas
no significant occupancy was detected at the negative control
necdin promoter (Fig. S1). The ChIP-seq analysis was conducted
via the standard mode of analysis for the ENCODE Consortium.
Immunoprecipitated DNA from two biological replicates was
analyzed by ChIP-seq. Because the replicates met established
criteria for reproducibility, the datasets were merged, and peaks
were called again. Unique sequences were mapped to the genome
(HG19). Replicates A and B yielded 9.9 million and 12.6 million
unique sequences, respectively. Using a false-discovery rate of
0.0001, replicates A and B yielded 18,233 and 8,830 GATA-2–
occupied loci (peaks), respectively. Comparison of replicates
revealed major overlap, with 7,619 (86%) of the B peaks present
in A. Merging the replicates yielded 15,529 peaks corresponding
Author contributions: A.K.L. and E.H.B. designed research; A.K.L. and H.O. performed
research; A.K.L., H.O., S.K., P.J.F., and E.H.B. analyzed data; and A.K.L., S.K., P.J.F., and
E.H.B. wrote the paper.
The authors declare no conflict of interest.
This article is a PNAS Direct Submission.
Data deposition: The data reported in this paper have been deposited in the Gene Ex-
pression Omnibus (GEO) database, www.ncbi.nlm.nih.gov/geo (accession no. GSE29531).
1To whom correspondence should be addressed. E-mail: email@example.com.
This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10.
| August 16, 2011
| vol. 108
| no. 33
to 6,081 genes (Dataset S1). The mean peak height was 75 bp
(range: 14–229 bp), and the mean width was 417 bp.
The GATA-2–occupied genes encode proteins known to be
important for endothelial cell function, including endothelial
NOS3, angiopoietins 1, 2, and 3, and intercellular adhesion
molecule-2 (Fig. 1). Certain genes encoded transcription factors,
including Kruppel-like factor-2 (KLF2) (Fig. 1). KLF2 is induced
by shear stress, mediates shear stress signaling (18), determines
vascular tone (19), mediates endothelial cell thrombotic function
(20), and promotes vessel maturation (21). KLF2 is repressed by
the proinflammatory cytokine IL-1β (18) and mediates the anti-
inflammatory actions of statins (22). Additional transcription
factors included the forkhead factor FOXP1 and multiple v-ets
erythroblastosis virus E26 oncogene homolog (ETS)/ETS-variant
(ETV) factors (Dataset S2), including ETV6, which is required
for hematopoiesis and maintenance of the vascular network (23,
24). GATA-2 occupied myocyte-specific enhancer factor 2C
(MEF2C), which is essential for vascular development (25).
MEF2C contains an endothelial enhancer, corresponding to the
GATA-2 peak at +139 kb, which directs endothelial cell-specific
expression of a reporter in E7.5–E9.5 mice (26). The ChIP-seq
analysis revealed a rich set of targets, including numerous genes
not linked previously to endothelial cell biology.
The majority of GATA-2–occupied peaks (13,256 peaks, 85%)
were not occupied in our prior K562 cell analysis (12) (Fig. 2A).
In HUVEC and K562 cells (12), the majority of peaks resided
distal to the genes in putative enhancer regions (Fig. 2B). The
unique chromatin-bound sites in HUVEC vs. K562 cells indi-
cates that GATA-2 occupies a very small subset of genomic
GATA motifs in a cell type-specific manner. To explore the re-
lationship between GATA-2 occupancy and the epigenome, we
evaluated histone marks in HUVEC at occupied sites. This
analysis revealed that 91% of GATA-2–occupied sites overlap
with H3K4me1 (Fig. 2C), a diagnostic enhancer mark (27). A
smaller subset of occupied sites (35%) overlaps with H3K4me3
(Fig. 2D). As expected for a mark associated with transcrip-
tion (28), 61% of these overlapping peaks reside in introns and
exons. A small cohort of occupied sites overlaps with H3K27me3
(Fig. 2E), H3K36me3 (Fig. 2F), and H4K20me1 (Fig. 2G) (1%,
3%, and 1%, respectively).
Linking GATA-2 with the Inflammatory Regulator AP-1. We assessed
whether the GATA-2 cistrome in HUVEC resembles that of
K562 cells, in which the majority of GATA-2–occupied peaks
reside at sites containing the GATA motif (12). Of the 15,529
GATA-2 peaks, 12,930 (83%) contained at least one WGATAR
motif. Of the 6,976,111 GATA motifs in the human genome,
25,823 (0.37%) resided within the 15,529 peaks. Constrained de
novo motif finding with Cosmo (29) identified WGATAA that
we defined previously for GATA-1 occupancy in K562 cells (Fig.
3A) (12). A broader GATA consensus, [CG][AT]GATAA[GAC]
[GAC], resided at 8,176 (43.3%) of the GATA-2–occupied K562
cell peaks and 4,698 (35.4%) of the HUVEC peaks.
De novo motif finding using MEME (30) to pinpoint cis-ele-
ments at GATA-2–occupied sites in HUVEC revealed canonical
motifs for the inflammatory regulator AP-1 (14) (Fig. 3B) and c-
ETS (Fig. 3C), which has important functions in endothelial cells
(31). AP-1 motifs (TGA[G/C]TCA) (32) resided in 45.4% of the
HUVEC GATA-2 peaks (7,055 of 15,523 peaks). This was highly
significant (P < 0.01) relative to 16.3% of GATA-2 peaks in K562
cells (3,451 of 21,167 peaks). Because 69% of GATA-2–occupied
peaks overlapped with those occupied by the jun proto-oncogene
(c-JUN) component of AP-1 (Fig. 3D and Dataset S3), 29%
overlapped with FBJ murine osteosarcoma viral oncogene ho-
molog (c-FOS) (Fig. 3E and Dataset S4), and only 1% (137)
overlapped with peaks of c-Myc occupancy (23,525 total c-Myc
peaks), this GATA-2–AP-1 link has broad importance. The lo-
cation with respect to neighboring genes of sites containing both
GATA-2 and c-JUN did not differ significantly from the location
of the complete cohort of GATA-2 occupancy sites. Examples of
AP-1 target genes in which GATA-2 overlapped with c-JUN and
c-FOS include IL-8 (33), CXCL2 (34), and CSF2 (35). GATA-2,
an AP-1 target (Fig. 3F). A high fraction of chromatin sites bound
by both GATA-2 and c-Jun were enriched in H3K4me1 or
H3K4me3 (76 and 83%, respectively).
We compared the frequency in which the c-ETS motif resided
Endothelial Cell Function Transcription Factors
occupancy sites in HUVEC identified by ChIP-seq. Genes important for endo-
2 target gene (kb).
occupied peaks in HUVEC and K562 cells. (B) Locations of GATA-2–occupied
peaks relative to nearest-neighbor genes determined with the Cis Element
Annotation System (http://liulab.dfci.harvard.edu/CEAS/; http://ceas.cbi.pku.
edu.cn/). (C–G) Comparison of GATA-2–occupied peaks and peaks of H3K4me1
(C), H3K4me3 (D), H3K27me3 (E), H3K36me3 (F), and H4K20me1 (G).
Computational mining of ChIP-seq data. (A) Comparison of GATA-2–
| www.pnas.org/cgi/doi/10.1073/pnas.1108440108Linnemann et al.
5,446 (35.1%) of the 15,523 GATA-2–occupied sites contained c-
ETS motifs. In K562 cells, 2,737 (12.9%) of the 21,167 GATA-2–
occupied sites contained c-ETS motifs. Based on a binomial test
for proportions, the differential frequency of c-ETS motifs at
GATA-2–occupied sites in HUVEC vs. K562 cells was highly
significant (P < 2.2e−16). Only 2,249 (14.5%) of the 15,523
GATA-2–occupied sites in HUVEC contained both c-ETS and
AP-1 motifs. A GATA-2 peak was more likely to contain an AP-1
motif if it lacked a c-ETS motif (P < 0.001). Thus, it is likely that
GATA-2 does not function frequently as a ternary complex with
AP-1 and c-ETS in HUVEC.
Direct GATA-2 Target Gene Ensemble in Endothelium. The frequent
occurrence of AP-1 motifs at GATA-2–occupied sites and
suggest that GATA-2 and AP-1 interact functionally. We used
RNA interference to establish GATA-2 target genes in HUVEC.
GATA2 mRNA was considerably lower in HUVEC than in K562
cells (Fig. 4A) (12), but endogenous GATA-2 in HUVEC was
detectable by Western blotting (Fig. 4C). GATA2 siRNA signifi-
siRNA-transfected cells (Fig. 4 B and C) without overtly affecting
HUVEC morphology. Expression profiling of two biological
replicates was conducted, and genes dysregulated upon GATA-2
knockdown were analyzed further. The direct GATA-2 targets
were identified by comparing the differential expression with
GATA-2 occupancy determined by ChIP-seq. This analysis
revealed 116 direct targets differentially expressed by twofold or
more upon GATA-2 knockdown (Fig. 4D). Because knockdowns
do not eliminate expression, the analysis underestimates the tar-
get gene ensemble.
The direct targets encode regulators of cell migration, in-
cluding semaphorin 3A (36), and podocalyxin-like (37). GATA-
2–activated direct targets encode proinflammatory mediators,
including interleukins, the chemokine ligand CXCL2, and the
1 2 3 4 5 6 7 8 9 1011
motifs from GATA-2–occupied ChIP-seq peaks: (A) GATA motif from con-
strained motif analysis and (B) AP-1 and (C) c-ETS motifs from de novo motif
finding. (D and E) Comparison of GATA-2–occupied peaks and peaks of (D)
c-JUN and (E) c-FOS occupancy in HUVEC. (F) Representative profiles dem-
onstrating overlap among GATA-2, c-JUN, and c-FOS occupancy peaks.
Linking GATA-2 and AP-1 function. (A–C) Logos of overrepresented
ChIP-seq Peak Height
Quantitative ChIP Signal
R = 0.68
TNFAIP3 (+28 kb)
TNFAIP3 (-170 kb)
CSF2 (-3 kb)
CSF2 (+13 kb)
RUNX1 (int3 peak1)
RUNX1 (-146 kb)
RUNX1 (-268 kb)
RUNX1 (-428 kb)
RUNX1 (-434 kb)
RUNX1 (-477 kb)
PODXL (-30 kb)
PODXL (-67 kb)
PODXL (-339 kb)
RNF149 (-21 kb)
KLF2 (-8 kb)
KLF2 (+27 kb)
HS3ST1 (-77 kb)
HS3ST1 (-120 kb)
HS3ST1 (-475 kb)
HS3ST1 (-476 kb)
RUNX1 (int3 peak2)
M x 10
levels in HUVEC and K562 cells. (B) Quantitative RT-PCR analysis of GATA2
transcript levels after treatment with nontargeting control siRNA or GATA2
siRNA. (C) Western blotting of GATA-2 in HUVEC transfected with control or
GATA2 siRNA. Whole-cell samples were analyzed. *, nonspecific band. (D)
Heat map depicting the mean fold change of expression at GATA-2–occu-
pied loci resulting from GATA2 knockdown (n = 2). (E and F) Quantitative
RT-PCR validation of array results in GATA-2–activated (down-regulated
with knockdown) (E) and repressed (up-regulated) genes (F). (G) Quantita-
tive ChIP validation of GATA-2 occupancy at loci dysregulated by the
knockdown. Data shown are mean ± SE; n = 3. (H) Correlation between
height of ChIP-seq peak and quantitative ChIP signal.
Direct GATA-2 target gene ensemble. (A) Comparison of GATA2
Linnemann et al.PNAS
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cytokine CSF2 (GM-CSF). GATA-2 directly repressed KLF2,
the key mediator of shear stress signaling in endothelium that
opposes inflammation. GATA-2 activated RSPO3, which con-
trols placental vascular plexus development and regulates an-
giogenesis during embryogenesis (38). Secreted RSPO3 activates
wingless-type/β-catenin signaling, which exerts a proinflammatory
function (38). GATA-2 induced expression of activating tran-
scription factor 3 (ATF3), a stress-regulated transcription factor
that protects endothelial cells from TNF-α–induced cell death
(39) and negatively regulates allergic airway inflammation (40).
A subset of GATA-2–activated and –repressed targets was sub-
jected to additional validation by real-time RT-PCR (Fig. 4 E and
F) and quantitative ChIP analysis using the human β-globin (HBB)
promoter as a negative control (Fig. 4G). ChIP-seq peak height
correlated (R2= 0.68) with the quantitative ChIP signal (Fig. 4H).
Our results indicate that GATA-2, c-JUN, and c-FOS com-
monly occupy the same chromatin sites containing GATA and
AP-1 motifs. Taken together with the fact that AP-1 is a key
regulator of inflammatory genes such as IL-8 and CSF2 that are
GATA-2 targets (Fig. 4 D and E), this finding suggests the ex-
istence of a GATA factor–AP-1 regulatory network to control
inflammatory genes. Ontological analysis of the direct GATA-2
target-gene ensemble revealed an enrichment of inflammatory
genes, the majority of which are GATA-2–activated (Fig. 5A).
GATA-2 Requirement for Assembly of Functional AP-1 Complexes on
Chromatin. In our loss-of-function analysis, HUVEC were cul-
tured in medium containing FBS, EGF, and FGF2, which induce
signaling that can activate AP-1 (13). Our results suggested that
GATA-2 and AP-1 confer maximal AP-1 target gene transcrip-
tion. We tested whether activity of presumptive AP-1 targets
requires AP-1 in HUVEC. Transfection with dominant-negative
c-FOS (A-FOS) (41, 42) significantly reduced IL-8 (P < 0.01),
CSF2 (P < 0.01), and IL-6 (P < 0.01) expression (Fig. 5C). We
tested genes that emerged from our direct GATA-2 target screen
that were not established AP-1 targets. Expression of RSPO3 (P <
0.01),CXCL1,and RUNX1 was reduced by A-FOS. c-JUN, and c-
FOS knockdowns confirmed AP-1 regulation of select GATA-2
targets; CSF2 and CXCL1 expression were not affected signifi-
cantly (Fig. 5 B and C). Thus, GATA-2 and AP-1 ensure maximal
activity of a cohort of the target genes.
We considered potential mechanisms underlying the GATA-2–
AP-1 cooperation (Fig. 6A). At target genes (Fig. 6AI), reduced
GATA-2 occupancy might decrease AP-1 occupancy (Fig. 6AII),
decrease AP-1 phosphorylation (Fig. 6AIII), or induce AP-1–
independent molecular alterations (Fig. 6AIV). To distinguish
among these possibilities, occupancy of c-FOS, c-JUN, and c-JUN
phosphorylated at serine 73 was analyzed in control siRNA- and
GATA2 siRNA-transfected cells. The analysis was conducted un-
der conditions in which GATA2 expression was greatly reduced
(Fig. 6B). Knocking down GATA-2 significantly reduced phos-
ATF3 genes (Fig. 6D). Loss of chromatin-bound phosphorylated
c-JUN was not associated with a change in total c-JUN (Fig. 6C).
To assess the specificity of the GATA-2–dependent phosphor-
insensitive IL-1B and IL-17D genes (Fig. 6D). GATA-2 knock-
down did not affect phosphorylated c-JUN occupancy at these
genes. Occupancy of all factors was low at the HBB promoter and
an unoccupied site 1 kb upstream of the IL-8 promoter (Fig. 6D).
The context-dependent GATA-2 requirement for phosphorylated
c-JUN occupancy may explain why certain AP-1 target genes that
are occupied by GATA-2 and AP-1 (Datasets S3 and S4) are in-
2 and c-JUN can interact (43). Coimmunoprecipitation analysis
with HUVEC whole-cell lysates did not provide evidence for
a stable complex containing GATA-2 and c-JUN.
We also tested whether c-JUN promotes GATA-2 chromatin
occupancy, but knocking down c-JUN did not affect GATA-2
chromatin occupancy (Fig. S2).
To test whether GATA-2 facilitation of AP-1–mediated tran-
scription has biological implications, we asked whether GATA-2
regulates secretion of the critical inflammatory mediator IL-8.
GATA-2–knockdown HUVECs were treated with the proin-
flammatory factor TNF-α, which induced IL-8 secretion several
fold as measured by ELISA (Fig. 6E). Knocking down GATA-2
significantly reduced IL-8 secretion (P = 0.0015), consistent with
the mRNA expression data.
Linking GATA-2, AP-1, and Inflammatory Genes. The results de-
scribed herein, representing the genome-wide analysis of a GATA
factor in endothelium, provide a rigorous genetic framework for
understanding GATA-2 function in vascular biology. GATA-2
controls expression of genes involved in establishing endothelial
cell phenotypes and inflammation. Analysis of 116 GATA-2–
occupied genes differentially regulated upon knocking down
com/products/pathways_analysis.html) revealed a complex net-
work (Fig. S3) in which factors were knitted together based on
established protein–protein interactions and abilities to regulate
expression of other factors. The network highlights a GATA-2
function in regulating genes mediating inflammation (Fig. 6F).
GATA-2 targets also included genes encoding anti-inflammatory
factors, including TNFAIP3, which is induced by proinflammatory
factors and suppresses proinflammatory signaling (44, 45) and
ATF3 (40),asdescribedabove. Certain AP-1 targets areregulated
by the proinflammatory factor NF-κB (46). Scanning the GATA-2
peaks with the NF-κB position/weight matrix from the JASPAR
database using the FIMO tool (47) revealed that only 9.1% of the
Programmed cell death ligand
Receptor for C-terminal cell binding domain of thrombospondin
Decay accelerating factor for complement
Cytokine; colony stimulating factor 2 (granulocyte-macrophage)
Modulates PDGF signaling; upregulated after vascular injury
Histone H4 family member
Interleukin receptor; induced by proinflammatory stimuli
Inflammation and B cell maturation
Chemoattractant, angiogenic factor, and inflammatory response mediator
Erythroid growth/differentiation factor; regulates osteotrophic factors
Reduces human lung endothelial inflammation
Functions pleiotropically; cell migration
Represses tumor necrosis factor alpha
I-kappa-B-beta; inhibits NF-kappa B complex
Regulation of T cell activation
Extracellular matrix degradation; possibly tumor cell migration and proliferation
Modulates cell motililty; chemoattractant
Placental plasminogen activator inhibitor
Cytokine; suppression of IL-2-dependent T-cell growth
Induced by TNF; Inhibits NF-kappa-B activation and TNF-mediated apoptosis
Putative transcription factor; TPA-responsive gene
Mr x 10-3
Mr x 10-3
(% of Control)
IL8 IL6CSF2RSPO3 CXCL1RUNX1
(% of Control)
IL8IL6 CSF2 RSPO3 CXCL1 RUNX1c-JUN
(% of Control)
IL8IL6 CSF2 RSPO3 CXCL1 RUNX1c-FOS
flammatory genes. (A) Genes involved in inflam-
mation and their expression changes with GATA-
2 knockdown. Blue, down-regulated with GATA-2
knockdown; Yellow, up-regulated with GATA-2
knockdown. (B) Western blotting of endogenous c-
JUN and c-FOS in HUVEC transfected with c-JUN or c-
FOS siRNA, respectively. Transfected cells were boiled
in SDS-sample buffer, and whole-cell samples were
Real-time RT-PCR analysis of gene expression in
HUVEC transfected with a dominant-negative AP-1
antagonist (A-FOS), c-JUN siRNA, or c-FOS siRNA. The
expression of empty vector-transfected cells (A-FOS)
and c-FOS) was designated as 100%, and expression
for each gene is represented as a percentage of con-
trol expression. Data shown are mean ± SE; n ≥ 3.
GATA-2/AP-1–dependent regulation of in-
| www.pnas.org/cgi/doi/10.1073/pnas.1108440108Linnemann et al.
peaks match significantly (P ≤ 1e−4) to the matrix, a finding that
is inconsistent with a frequent GATA-2–NF-κB association at
HUVEC chromatin sites.
Because certain GATA-2 target genes with established roles in
inflammation were co-occupied by GATA-2, c-JUN, and c-FOS,
GATA-2 knockdown reduced phosphorylated c-JUN occupancy
it is attractive to propose that GATA-2–AP-1 cooperation is a
common mode of orchestrating inflammatory processes. Vascular
endothelial dysfunction involving inflammation underlies diseases
including atherosclerosis, rheumatoid arthritis, and type II di-
Given the biological importance of genes constituting the
GATA-2 genetic network in HUVEC, why does the Gata2-
knockout mouse appear to have morphologically normal vascu-
lature, despite dying at E10.5 because of severe anemia (3)? Since
multiple GATA factors are expressed in endothelium (48), a rig-
orous loss-of-function approach to ascertain GATA-2 function in
the adult vasculature might require multigenic disruptions to re-
veal critical GATA factor-dependent pathways; alternatively, an
important vascular activity of GATA-2 might not be apparent
given the early embryonic lethality.
The direct regulation of inflammatory genes by GATA-2 has
considerable biological and pathophysiological implications. It
will be important to implement additional functional analyses
and to determine whether endothelial cell subtypes differ in this
mechanism. During the review of our manuscript, a genome-wide
analysis of GATA-2 occupancy in human microvascular endo-
thelial cells (HMVEC) was reported (49). Comparison of the
HUVEC and HMVEC datasets will be informative.
Given the oncogenic activity of AP-1 subunits (50, 51), the role
for GATA-3 in breast cancer (52), and links between GATA-2
whether GATA factors cooperate with AP-1 in diverse biological
contexts and to what extent disruption of this mechanism under-
lies human pathologies. Because strong evidence implicates in-
flammation in a plethora of disease processes, including cancer,
studies to evaluate GATA-2 function in endothelium in diverse
disease models will be particularly instructive.
Materials and Methods
Cell Culture. HUVEC were maintained in Medium 200 (Cascade Biologics)
antibiotic/antimycotic (Invitrogen). Cells were passaged at ∼70–80% conflu-
ence with 0.05% trypsin, and cells from passages four and six were used.
Antibodies. Rabbit anti–GATA-2 polyclonal antibody was described pre-
viously (54). Anti–c-FOS (SC-253) and anti–c-JUN (SC-45) were from Santa
Cruz Biotech. Anti–p-c-JUN (Ser73) was from Cell Signaling Technology
(#9164). Mouse monoclonal α-tubulin (#CP06) was from Calbiochem.
Transfection and RNA Interference. HUVEC were seeded 3–5 d before siRNA
or plasmid transfection and grown to ∼70% confluence. After trypsiniza-
tion, ∼106cells per experiment were resuspended in HUVEC Nucleofector
Solution (Lonza), and 0.45–0.60 nmol of siRNA, 5 μg A-FOS expression vector,
or empty vector was transfected using the Nucleofector system (Lonza).
siGENOME SMARTpool GATA2 siRNA, c-JUN siRNA, c-FOS siRNA, and control
siRNA (SMARTpool #1) were from Dharmacon (Thermo Scientific). CMV-500-
A-Fos and CMV-500 empty vector were provided by Charles Vinson (National
Cancer Institute, Bethesda, MD). Cells were analyzed 24 or 40 h after siRNA
or A-Fos transfection, respectively.
Generation of ChIP-Seq Data. HUVEC histone ChIP-seq data were generated
at the Broad Institute and by the B. Bernstein group (Massachusetts General
Hospital, Boston, MA). HUVEC c-JUN ChIP-seq data were generated by
D. Raha and Mike Snyder (Stanford University, Stanford, CA). HUVEC c-Myc
ChIP-seq dataweregeneratedbyVishy Iyer(UniversityofTexas atAustin, TX).
HUVEC GATA-2, HUVEC c-FOS, and K562 GATA-2 ChIP-seq data were gener-
ated by the P. Farnham group. These data were collected as part of the EN-
CODE Consortium (http://www.genome.gov/10005107) and are available on
the University of California, Santa Cruz Genome Browser.
Quantitative ChIP Analysis. ChIP was conducted as described (55) using 1 × 108
HUVEC cells per condition for ChIP-seq, or 1 × 107HUVEC cells per condition
for quantitative ChIP.
ChIP-Seq Analysis. HUVEC were crosslinked for 10 min with 1% formaldehyde,
snap frozen, and stored at −80 °C. ChIP was conducted as described (http://
Relative mRNA Levels
Mr x 10-3
Loss of AP-1Loss of PhosphorylationIndependent
PI c-FOS c-JUN p-c-JUN
IL8 (-1 kb)
PIc-FOS c-JUN p-c-JUN
RSPO3 (+28 kb, intron 1)
ATF3 (-12 kb)
chromatin. (A) Models as described in Results and Discussion. (B) Real-time
RT-PCR quantitation of GATA2 mRNA. (C) Western blotting of c-JUN phos-
phorylated at serine 73 [p-c-JUN (Ser73)] in HUVEC transfected with control
or GATA2 siRNA. Whole-cell samples were analyzed by Western blotting. (D)
Quantitative ChIP of preimmune control (PI), c-FOS, c-JUN, and p-c-JUN (Ser-
73) occupancy at the IL-8 promoter, RSPO3, and ATF3, AP-1 targets that are
GATA-2–independent (IL-1B, IL-17D), and negative controls (HBB and a site 1
kb upstream of the IL-8 promoter). Data shown are mean ± SE; n ≥ 3. (E)
ELISA quantitation of IL-8 levels in medium from control siRNA- or GATA-2
siRNA-transfected cells treated with 10 ng/mL TNF-α or vehicle for 6 h. Data
shown are mean ± SE; n = 6. (F) GATA-2–AP-1 coregulation of inflammatory
genes in endothelium.
GATA-2 requirement for assembly of functional AP-1 complexes on
Linnemann et al.PNAS
| August 16, 2011
| vol. 108
| no. 33
www.genomecenter.ucdavis.edu/farnham/pdf/FarnhamLabChIP%20Protocol. Download full-text
pdf). Chromatin from 108cells was diluted with three volumes of immuno-
precipitation (IP) dilution buffer and incubated at 4 °C overnight with 60 μL of
rabbit anti–GATA-2 antibody. StaphA cells were blocked with BSA. StaphA
and Staph-Seq cells (SIGMA) were used. Blocked StaphA/Staph-Seq cells (100
μL) were added to the antibody/chromatin mixture and rotated for 15 min at
room temperature. StaphA/Staph-Seq cells were washed twice with dialysis
buffer and four times with polyclonal IP wash buffer. After reversal of cross-
links and RNase treatment, DNA was purified and analyzed as described with
the Illumina GA2 platform (56). Short-sequence reads were aligned to the
genome, and peaks were called using Sole-search (false-discovery rate, 0.0001;
α value 0.001) with sequenced HUVEC input DNA as background (57). ChIP-seq
dataproducedbytheENCODEProjectConsortiumweredownloaded from the
University of California, Santa Cruz browser and analyzed with Sole-search.
Peak overlap was analyzed with Sole-search, allowing a distance of 200 bp
Expression Profiling. Control siRNA- and GATA2 siRNA-treated samples col-
lected 24 h posttransfection were analyzed by hybridization to human 244k
expression microarrays (Agilent Technologies). For each sample, 1.2 μg RNA
was labeled with Cy-5 (control siRNA) or Cy-3 (GATA2 siRNA), and labeled
cRNAs were combined and hybridized. Data were collected with an Agilent
scanner. Two biological replicates for control siRNA/GATA-2 siRNA pairs
were each hybridized in duplicate, yielding similar results, and average sig-
nals between the four replicates were used to establish fold-change cutoffs.
PCR Primers. Primer sequences are indicated in Dataset S5.
ACKNOWLEDGMENTS. We thank Dr. Charles Vinson (National Institutes of
Health) for providing the A-Fos vector. ChIP-seq was supported by National
Human Genome Research Institute funds. We acknowledge funding from
National Institutes of Health Grants R21 HL091520 and DK068634 (to E.H.B.),
HG003747 (to S.K.), and 1U54HG004558 (to P.J.F.). A.K.L. is an American
Heart Association postdoctoral fellow.
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| www.pnas.org/cgi/doi/10.1073/pnas.1108440108Linnemann et al.