Abstract
Bromodomains (BRDs) have emerged as compelling targets for cancer
therapy. The development of selective and potent BET (bromo and
extra-terminal) inhibitors and their significant activity in diverse
tumor models have rapidly translated into clinical studies and have
motivated drug development efforts targeting non-BET BRDs. However, the
complex multidomain/subunit architecture of BRD protein complexes
complicates predictions of the consequences of their pharmacological
targeting. To address this issue, we developed a promiscuous BRD
inhibitor [bromosporine (BSP)] that broadly targets BRDs (including
BETs) with nanomolar affinity, creating a tool for the identification
of cellular processes and diseases where BRDs have a regulatory
function. As a proof of principle, we studied the effects of BSP on
leukemic cell lines known to be sensitive to BET inhibition and found,
as expected, strong antiproliferative activity. Comparison of the
modulation of transcriptional profiles by BSP after a short exposure to
the inhibitor resulted in a BET inhibitor signature but no significant
additional changes in transcription that could account for inhibition
of other BRDs. Thus, nonselective targeting of BRDs identified BETs,
but not other BRDs, as master regulators of context-dependent primary
transcription response.
INTRODUCTION
Bromodomains (BRDs) are acetyl-lysine–dependent protein interaction
modules that play a pivotal role in chromatin biology and control of
gene expression. The human BRD family comprises 61 diverse domains that
are present in mainly nuclear proteins. They often act as scaffolding
proteins but may also have catalytic functions, such as adenosine
triphosphatase–dependent helicase, histone acetyl, or methyl
transferase activities ([68]1). Acetyl-lysine recognition is mediated
by a binding cleft formed by four canonical helices (αZ, αA, αB, and
αC) and two connecting loop regions (ZA and BC loops). This interaction
site is highly druggable, enabling the development of potent protein
interaction inhibitors ([69]2, [70]3). BRD-containing proteins have
been linked to a diversity of diseases, in particular to cancer ([71]2,
[72]4). The first inhibitors developed to target BET (bromo and
extra-terminal) proteins showed potent activity not only in cancers
that are dependent on chromosomal rearrangements involving BETs but
also in other diverse cancer types ([73]5, [74]6). This unexpected
finding has been rationalized by the specific transcriptional
down-regulation of growth-promoting and antiapoptotic genes, including
key oncogenes, such as c-Myc ([75]6). The strong growth-inhibiting
properties of BET inhibitors therefore rapidly translated into clinical
studies that aim to examine their efficacy in oncology ([76]2).
The success of BET inhibitors led to the development of programs
targeting other BRD proteins, and a number of potent and selective
chemical probes have now been published ([77]5, [78]7–[79]12). However,
to date, few phenotypic consequences of the inhibition of non-BET BRDs
have been reported. To evaluate the benefits of targeting other BRDs,
we developed a promiscuous BRD inhibitor with nanomolar potency for 13
BRDs and low micromolar activity for 12 additional BRDs. In analogy
with the nonspecific kinase inhibitor staurosporine ([80]13, [81]14),
we named this promiscuous inhibitor bromosporine (BSP). We evaluated
the consequences of BSP on transcription in leukemic cell lines, a
cancer type that has been studied well using BET inhibitors ([82]6). We
found that BSP had effects on cell proliferation and clonogenic growth
that were similar to those of the pan-BET inhibitor (+)-JQ1 (henceforth
JQ1). Genome-wide transcriptional analysis using Illumina microarrays
revealed a pronounced BET signature after a short exposure to BSP. In
agreement with these data, the selective inhibitors of non-BET BRDs
showed only negligible effects on gene transcription, suggesting that
BETs—and not any of the other BRDs inhibited by BSP—are dominant
mediators of primary transcription response in leukemia. We believe
that BSP, similar to staurosporine, will be a versatile tool for
studying protein acetylation in chemical biology and will inspire the
development of selective BRD inhibitors using related scaffolds.
For the design of BSP, we analyzed a comprehensive collection of BRD
structures and complexes of BRDs with histone peptides ([83]1,
[84]15–[85]20). This analysis revealed similar binding modes of
histone-derived peptides across different BRD structural classes. A
channel formed by the ZA loop and helix A is present in most BRD
structures, but this pocket is rarely occupied by peptidic histone
ligands (fig. S1A). We therefore hypothesized that BRD inhibitors with
broad activity against BRDs could target this conserved groove because
it offers little possibility for sequence-specific peptide binding
while enhancing inhibitor affinity.
Expanding on our previous work on nonselective inhibitors based on a
tricyclic chemotype ([86]21), we selected a similar triazolopyridazine
dicyclic core scaffold to explore the development of potent promiscuous
inhibitors. We rationalized that scaffold expansion toward the
identified binding groove and toward the BC loop would help avoid
subfamily-unique features, such as the WPF [tryptophan (W)–proline
(P)–phenylalanine (F)] shelf that would confer selectivity toward the
BET family (fig. S1B). Compounds developed as part of a small focused
library of dicyclic chemotypes, modified at two positions (fig. S1C),
exhibited broad activity in a thermal stability assay against a diverse
set of human BRD targets that comprised representative members of all
BRD structural subfamilies (fig. S2A). Several optimization cycles led
to a compound that potently inhibited most BRDs, which we named “BSP”
and selected for further characterization ([87]Fig. 1A, fig. S2B, and
table S1).
Fig. 1. BSP is a pan-BRD inhibitor in vitro.
Fig. 1
[88]Open in a new tab
(A) Triazolopyridazine scaffold of BSP. (B) 2F[o] − F[c] map of BSP
bound to BRD4(1) contoured at 2σ. (C) Complex of BSP with the BRD of
BRD9. The compound adopts an acetyl-lysine mimetic pose within the BRD
cavity, initiating interactions with the conserved asparagine (N100).
The sulfonamide function initiates contacts with ZA-loop residues
(G43), further stabilizing the interaction without displacing any of
the structurally conserved water molecules (red spheres). (D) BSP
binding with low micromolar to nanomolar affinity to most structural
classes within the human BRD family. Dissociation constants (K[D])
measured in-solution using ITC are displayed on the human BRD tree as
spheres (size and color as indicated in the inset). BRD structural
classes are annotated with roman numerals. (E) Overlay of ITC
measurements of typically strong BSP interactions with the BRDs of
CECR2, BRD9, and TAF1L(2). Raw injection heats for the titrations of
proteins into solutions of BSP are shown on the left panels. The right
panel shows the normalized binding enthalpies corrected for the heat of
protein dilution as a function of binding site saturation (symbols as
indicated in the figure). Solid lines represent a nonlinear
least-squares fit using a single-site binding model. All titrations
were carried out in 50 mM Hepes buffer (pH 7.5; 25°C) and 150 mM NaCl
at 15°C while stirring at 1000 rpm.
RESULTS
BSP is a potent pan-BRD inhibitor in vitro
To confirm the promiscuous targeting of the human BRD family by BSP, we
screened BSP with biolayer interferometry (BLI) against a panel of 42
recombinant biotinylated BRDs that covered all structural families
(fig. S2C). In agreement with our thermal melt data, BSP showed broad
activity against the BRDs of diverse families. To ascertain the
predicted promiscuous binding mode, we determined high-resolution
crystal structures with different human BRDs. In all cases, BSP was
resolved well in the high-resolution structures ([89]Fig. 1B) and
inserted into the acetyl-lysine binding cavity of each BRD module. The
binding mode was conserved between BRD9 ([90]Fig. 1C), the first BRD of
BRD4 [BRD4(1); fig. S2D], and the second BRD of TAF1L [TAF1L(2); fig.
S2E]. In all cases, BSP exhibited a common binding mode, whereby it
engaged the conserved asparagine [N100 in BRD9, N140 in BRD4(1), and
N1602 in TAF1L(2)] while extending its sulfonamide substituent toward
the front of the ZA-loop binding cavity. The excellent shape
complementarity with diverse acetyl-lysine binding sites explained its
high binding potency, which we further characterized in solution by
isothermal titration calorimetry (ITC) ([91]Fig. 1, D and E). For
several target subfamilies, BSP represents the most potent inhibitor
developed to date ([92]Fig. 1D and [93]Table 1). BSP exhibited affinity
toward previously untargeted BRDs, such as TAF1L(2) (K[D] = 43 nM), and
provided a novel chemical starting point for BRDs that had been
previously targeted with only very weak compounds, such as
P300/CBP-associated factor (PCAF) (BSP K[D] = 4.7 μM).
Table 1. ITC of human BRDs with BSP.
Titrations were carried out in 50 mM Hepes buffer (pH 7.5; 25°C) and
150 mM NaCl at 15°C while stirring at 1000 rpm. Proteins were titrated
into the ligand solution (reverse titration). Values are means ± SD.
Protein [P] (μM) [L] (μM) K[D] (nM) ΔH^obs (kcal/mol) N TΔS (kcal/mol)
ΔG (kcal/mol)
BAZ2A 433 15 3,745 ± 291 −3.04 ± 0.095 1.05 ± 0.025 4.12 −7.16
BAZ2B 607 15 Weak binding
BRD1 440 13 1,653 ± 66 −7.32 ± 0.078 1.01 ± 0.008 0.30 −7.62
BRD2(1) 271 25 97.1 ± 6.7 −7.90 ± 0.034 1.00 ± 0.003 1.34 −9.25
BRD2(2) 235 25 50.3 ± 5.0 −5.46 ± 0.028 1.10 ± 0.003 4.18 −9.64
BRD3(1) 275 20 91.7 ± 5.3 −10.02 ± 0.039 1.00 ± 0.002 −0.95 −9.08
BRD3(2) 305 25 50.0 ± 4.7 −8.62 ± 0.041 1.11 ± 0.003 1.01 −9.63
BRD4(1) 258 20 41.8 ± 2.8 −11.09 ± 0.038 0.94 ± 0.002 −1.36 −9.73
BRD4(2) 270 20 39.7 ± 2.2 −6.60 ± 0.018 0.94 ± 0.001 3.17 −9.77
BRDT(1) 228 20 40.2 ± 2.8 −13.16 ± 0.047 1.02 ± 0.002 −3.40 −9.76
BRDT(2) 271 20 172.1 ± 10.6 −5.61 ± 0.028 1.00 ± 0.003 3.31 −8.92
BRD9 251 25 41.7 ± 3.8 −8.75 ± 0.039 1.00 ± 0.002 0.98 −9.73
BRPF1B 406 20 311.5 ± 11.2 −6.12 ± 0.021 1.00 ± 0.003 2.45 −8.57
BRPF3 400 15 8,621 ± 381 −4.20 ± 0.123 1.08 ± 0.025 2.48 −6.68
CECR2 202 16 8.0 ± 1.0 −17.28 ± 0.062 1.04 ± 0.002 −6.60 −10.68
CREBBP 617 25 1,524 ± 116 −2.91 ± 0.041 1.03 ± 0.011 4.72 −7.64
EP300 460 15 7,194 ± 501 −5.65 ± 0.265 0.97 ± 0.036 1.13 −6.78
BPTF 230 15 1,887 ± 53 −10.09 ± 0.074 1.07 ± 0.005 −3.28 −6.80
GCN5L2 336 15 Weak binding
PB1(3) 389 15 Weak binding
PB1(5) 604 15 14,225 ± 802 −3.19 ± 0.183 1.09 ± 0.053 3.20 −6.39
PCAF 610 13 4,762 ± 459 −7.85 ± 0.454 0.95 ± 0.044 −0.83 −7.02
SMARCA2 222 25 Weak binding
SMARCA4 400 13 19,685 ± 838 −9.05 ± 0.580 0.99 ± 0.055 −2.84 −6.21
TAF1(1) 460 23 5,525 ± 199 −3.34 ± 0.046 0.98 ± 0.010 3.60 −6.94
TAF1(2) 230 20 16.6 ± 2.7 −14.05 ± 0.095 0.98 ± 0.003 −3.80 −10.25
TAF1L(1) 610 13 25,000 ± 2,027 −4.78 ± 0.778 1.00 ± 0.146 1.29 −6.07
TAF1L(2) 250 20 42.7 ± 4.4 −12.48 ± 0.069 0.99 ± 0.003 −2.76 −9.72
TIF1A 400 15 8,475 ± 431 −2.61 ± 0.088 1.09 ± 0.029 4.06 −6.67
[94]Open in a new tab
BSP engages its target BRDs in cells
Next, we were interested to see whether BSP would exhibit similar
promiscuous binding to BRD-containing proteins in cells. To address
this, we synthesized BSP adducts with biotin tethered by a flexible
linker. The versatile pyridazine portion of BSP allowed positioning of
a tag that would point away from the central BRD cavity ([95]Fig. 2A),
resulting in two biotinylated variants ([96]Fig. 2B), which largely
retained the in vitro affinity toward BRDs ([97]Table 2). The
biotinylated BSP variants could engage with CECR2 in human embryonic
kidney (HEK) 293 cells stably expressing 3×FLAG CECR2 ([98]Fig. 2C).
Encouraged by this result, we used the biotin tag to extract and purify
BRDs, applying mass spectrometry (MS) together with pull-down
experiments using BSP biotin adducts. Although most BRD-containing
proteins are expressed in HEK293 cells, we observed enriched binding to
most high-affinity in vitro targets of BSP ([99]Fig. 2D and [100]Table
3) and also identified binding partners from larger complexes, such as
the SWI/SNF BRD-containing proteins SMARCA2/SMARCA4 and PB1, which were
most likely enriched as a result of a tight interaction with the BSP
targets BRD7/BRD9 ([101]22). We next asked whether BSP directly engages
its BRD targets in cells in the acetyl-lysine competitive mode of
action suggested by our structural models. We used a fluorescence
recovery after photobleaching (FRAP) technique to disrupt the
interaction of full-length green fluorescent protein (GFP)–tagged BRD4
([102]Fig. 2, E and F) or full-length GFP-tagged BRD9 ([103]Fig. 2, G
and H) with acetylated chromatin. In both cases, we observed
displacement of proteins from chromatin, as evidenced by the fast
recovery after bleaching ([104]Fig. 2, F and H). In the case of BRD9,
we treated cells with the histone deacetylase inhibitor SAHA
(suberoylanilide hydroxamic acid) to increase acetylation levels and to
enhance binding ([105]Fig. 2H). For a representative selection of BRDs
tested in the presence of BSP, we observed a significant shortening of
fluorescence recovery times indicative of inhibition of chromatin-BRD
interaction.
Fig. 2. BSP engages its target BRDs in cells.
Fig. 2
[106]Open in a new tab
(A) BSP binds to the BRD acetyl-lysine cavity, allowing for further
functionalization toward the front channel within the ZA loop (Ra
vector annotated in orange) or the back of the pocket (Rb vector
annotated in orange). The vectors are shown in the complex of BSP with
BRD4(1). (B) Two variants of biotinylated BSP (BSP-a and BSP-b) were
prepared to explore binding to human BRDs in cells by pull-down
experiments. (C) Biotinylated BSP (BSP-a or BSP-b) immobilized on
magnetic beads was used to pull down human CECR2 from Flp-In T-REx
HEK293 cells stably expressing 3×FLAG CECR2. The protein captured from
whole-cell lysate was identified using anti-FLAG. (D) Cell lysate from
HEK293T cells was incubated with biotinylated BSP (BSP-a or BSP-b)
immobilized on magnetic streptavidin beads in the presence or absence
of 30 nmol of BSP for 2 hours at 4°C. After pull-down and tryptic
digestion with trypsin, proteins were identified in a TripleTOF 5600
mass spectrometer. (Top) Normalized abundance of each BRD-containing
protein in HEK293 cells (data from Proteomics DB;
[107]https://www.proteomicsdb.org/). (Bottom) Ratio of peptide to
peptide abundance in the presence and absence of competing BSP, shown
as a bar graph. BRD families are annotated with roman numerals. (E)
FRAP evaluation of full-length GFP-tagged BRD4 dissociation from
chromatin in U2OS cells. Nuclei of DMSO-treated (top) or BSP-treated (1
μM; bottom) cells. Target regions of photobleaching are indicated with
a white circle. Scale bar, 10 μm. FL-BRD4, full-length BRD4; FL-BRD9,
full-length BRD9. (F) Quantitative comparison of time to half-maximal
fluorescence recovery for BRD4 FRAP studies using BSP (red bars) as a
function of ligand concentration. (G) FRAP evaluation of full-length
GFP-tagged BRD9 dissociation from chromatin in U2OS cells. Nuclei of
DMSO-treated (top) or BSP-treated (1 μM; bottom) cells in the presence
of 10 μM SAHA (added to increase the experimental window). Target
regions of photobleaching are indicated with a white circle. Scale
bars, 10 μm. (H) Quantitative comparison of time to half-maximal
fluorescence recovery for BRD9 FRAP studies using BSP (red bars) as a
function of ligand concentration. Data in (F) and (H) represent means ±
SEM (n = 30) and are annotated with P values obtained from a two-tailed
t test (*P < 0.05 and ***P < 0.001).
Table 2. ΔT[m] shifts (°C) of biotinylated BSP (BSP-a and BSP-b) tested
against a panel of BET and other diverse BRDs.
Compounds (final concentration, 10 μM) were added to the proteins
(final concentration, 2 μM); the temperature was increased from 25° to
96°C at a step of 3°C/min; excitation and emission filters for the
SYPRO Orange dye were set to 465 and 590 nm; and experiments were
performed in triplicate. Values are means ± SD.
Protein ΔT[m]^obs (°C)
BSP-a BSP-b
BRD2(1) −7.5 ± 0.7 1.9 ± 0.5
BRD2(2) −0.4 ± 0.2 3.0 ± 0.2
BRD3(1) −2.7 ± 0.2 2.3 ± 0.3
BRD3(2) −3.0 ± 0.1 3.1 ± 0.0
BRD4(1) −2.7 ± 0.2 3.8 ± 0.3
BRD4(2) −5.1 ± 0.0 1.8 ± 0.3
BRDT(1) −5.0 ± 0.1 1.3 ± 0.3
BRDT(2) 0.5 ± 0.1 0.9 ± 0.4
CECR2 4.3 ± 0.0 7.4 ± 0.1
CREBBP −3.1 ± 0.6 0.1 ± 0.4
TAF1(2) −12.4 ± 0.1 1.9 ± 0.1
TAF1L(2) 0.2 ± 0.1 2.9 ± 0.4
[108]Open in a new tab
Table 3. Relative abundance of BRD-containing proteins in HEK293 cells (data
taken from Proteomics DB; [109]https://www.proteomicsdb.org/).
Pull-down of human BRD-containing proteins with biotinylated BSP (BSP-a
and BSP-b), followed by competitive elution with BSP and MS, resulted
in enrichment of BSP-targeted BRDs.
Protein Relative peptide abundance Protein Relative peptide abundance
293 BSP-a BSP-b 293 BSP-a BSP-b
CECR2 — — — BRD7 4.51 10 5
BPTF 4.31 — — BRD9 4.57 7.6 8
GCN5L2 3.75 — — SP140L — — —
PCAF — — — SP140 — — —
BRD2 5.59 8 2 SP100 3.43 — —
BRD3 5.04 2 2 SP110 2.71 — —
BRD4 5.17 3 2 TIF1a 4.91 — —
BRDT — — — TRIM33 5.34 — —
BAZ1A 5.19 — — TRIM66 — — —
BAZ1B 5.63 0.64 1.33 BAZ2A 4.25 — 3
BRWD3 3.61 — — BAZ2B 3.21 — —
PHIP 5.05 1.09 1.15 MLL 3.67 — —
BRWD1 3.24 — 4 TRIM28 7.05 — —
CREBBP 4.61 — — ZMYND8 4.4 — —
EP300 4.87 — — TAF1 — 16 —
BRD8 4.48 — — TAF1L — — —
ATAD2 4.65 0.98 0.87 ZMYND11 — — —
ATAD2B 3.68 0.90 1 ASH1L — — —
BRD1 3.48 — — PB1 5.23 8.28 8
BRPF1 3.6 — — SMARCA2 4.44 — —
BRPF3 3.42 — — SMARCA4 5.53 8.8 5
[110]Open in a new tab
BSP inhibits growth of cancer cell lines
To further assess the effects of BSP on cellular systems, we profiled
this inhibitor against the National Cancer Institute (NCI) panel of
cancer cell lines (NCI-60). BSP exhibited strong growth inhibition in
all cancer types (fig. S3, A to I), including leukemia (fig. S3D). We
were particularly interested in this cancer type because we previously
observed strong inhibition of the growth of leukemia cell lines when we
used the pan-BET inhibitors JQ1 and PFI-1 ([111]5, [112]7). We
therefore investigated the ability of BSP to inhibit the clonogenic
growth and proliferation of two acute myeloid leukemia (AML) cell lines
(MV4;11 and KASUMI-1), the hyperdiploid AML line OCI-AML3, and the
BCR-ABL–positive chronic myeloid leukemia (CML) cell line K562, and we
observed growth inhibition in the concentration range 100 to 500 nM
([113]Fig. 3A and fig. S3J). Colony formation by the cells was
decreased at 500 nM BSP and severely inhibited at 1 μM BSP ([114]Fig.
3B). Given the potent inhibition observed in colony formation, we also
compared the effect of BSP on clonogenic growth to the effect of the
pan-BET inhibitor JQ1, which was previously shown to potently and
effectively suppress proliferation in AML ([115]6). K562 cells were
relatively resistant to BSP, similar to JQ1, whereas we measured
nanomolar median inhibitory concentration (IC[50]) values for both
inhibitors in MV4;11 and KASUMI-1 cells (fig. S4A). In summary, our
data showed that BSP potently inhibited colony formation and
proliferation of leukemic cell lines with an efficacy that was slightly
weaker than that of pan-BET inhibitors, such as JQ1, in agreement with
its comparable in vitro potency toward BET family members.
Fig. 3. BSP inhibits growth in leukemia cell lines.
Fig. 3
[116]Open in a new tab
(A) BSP inhibits clonogenic growth in leukemia cell lines. K562,
KASUMI-1, MV4;11, and OCI-AML3 in methylcellulose were treated with
vehicle (DMSO) or BSP (0.1, 0.5, or 1 μM) (n = 4). (B) Colony formation
assay in K562, KASUMI-1, MV4;11, and OCI-AML3 cells using 0.1, 0.5, or
1.0 μM BSP (top) and the number of cells counted after treatment of
cells with BSP for 6 to 10 days (n = 4) (bottom). CFU, colony-forming
units; ns, not significant. (C) Similarity comparison of significantly
expressed genes (P < 0.001 and fold change > 1.5) in the four cell
lines after drug treatment. The heat map represents the intersect
matrix for all pairwise comparisons (cell lines and treatments) using
euclidean distances and complete linkage after transformation of the
intersect counts into similarity Jaccard measures. (D) Venn diagrams
showing overlap of the top statistically significant
(Benjamini-Hochberg adjusted P < 0.001) genes (up- or down-regulated
with a fold change of >1.5) differentially expressed by BSP or the
pan-BET inhibitor JQ1 in four leukemia cell lines (K562, KASUMI-1,
OCI-AML3, and MV4;11) after 8 hours of treatment with the inhibitor
(0.5 μM) (top) and breakdown of the expression in terms of up- and
down-regulated genes for each cell line (bottom). (E) Heat map of log
fold changes in the expression of the top 50 statistically significant
genes in the four cell lines tested, identified using
Benjamini-Hochberg adjusted P < 0.001. Data in (B) represent means ±
SEM (n = 4) and are annotated with P values obtained from a two-tailed
t test (*P < 0.05, **P < 0.01, ***P < 0.001, and ****P < 0.0001).
BSP modulates transcription in leukemic cell lines
To better understand the contribution of BRDs to the transcriptional
landscape in leukemia, we compared the primary effects on transcription
after a short exposure to BSP or JQ1 in the three sensitive AML cell
lines (MV4;11, KASUMI-1, and OCI-AML3) and in the less sensitive CML
cell line (K562). Principal components analysis revealed that gene
expression data sets of each cell line clustered together with no
obvious outliers, validating the quality of the gene expression data
(fig. S4B). Genes attenuated by either inhibitor were very similar
([117]Fig. 3C). Pairwise comparison of significantly up- and
down-regulated genes (P < 0.001 and fold change > 1.5) showed a strong
correlation between the two inhibitors, suggesting that BET BRDs may be
principally responsible for the observed effect on transcription
([118]Fig. 3D). Both inhibitors resulted in very similar fold changes
for the most significantly regulated (P < 0.001 and fold change > 1.5)
genes in each studied cell line, although the effects of BSP and JQ1 on
gene transcription were highly context-dependent ([119]Fig. 3E). In
contrast, comparison between sensitive cell lines and the less
sensitive K562 cells revealed significant differences in regulated
genes. Many genes with key functions in tumorigenesis, such as
transcription factors (GATA1, FOXA3, and HOXA5), tyrosine kinases
(CSF1R and FES), and apoptosis regulators (BCL2 and BCL6B), were
differentially regulated in highly BET inhibitor–sensitive cell lines
(MV4;11 and OCI-AML3) in comparison to the K562 line (fig. S4C).
Independent validation of the effects of BSP and JQ1 by quantitative
real-time polymerase chain reaction (qRT-PCR) confirmed inhibitor
effect in a dose-dependent fashion on the four leukemic cell lines
(fig. S4D). Among the most significantly deregulated genes were histone
clusters (linker, H2, and H3, including isoforms), which were generally
strongly up-regulated in K562 and down-regulated in BSP- and
JQ1-sensitive cells (fig. S5A). We suspected that a differential effect
on cell cycle may be a principal factor regulating sensitivity to BSP
in these cell lines. Transcription of histones is typically amplified
by 20- to 30-fold during the G[1]-to-S transition ([120]23). We
hypothesized that sensitive cells may be arrested before this
transition while less sensitive cells are cycling normally, resulting
in higher levels of histones. We therefore investigated the effect of
BSP on the cell cycle. Although BSP and JQ1 had little effect on cell
cycle in the broad range of concentrations tested in the less sensitive
K562 cells ([121]Fig. 4A), the sensitive MV4;11 cells were potently
affected and exhibited distinct G[1] arrest with reduced S-phase
populations ([122]Fig. 4B).
Fig. 4. Comparison of the effects of BET inhibition on cell cycle and
transcription in leukemias.
Fig. 4
[123]Open in a new tab
(A and B) Cell cycle analysis of BSP (blue scale) and JQ1 (red scale)
inhibition in resistant cells (A; K562) or sensitive cells (B; MV4;11)
after 48 hours of treatment with inhibitors (amounts as indicated in
the inset). The quantification given below each graph depicts the
percent S-phase content measured under each condition, indicating a
clear arrest in the sensitive line with minor effects on the resistant
line. Data are means ± SEM (n = 3) and are annotated with P values
obtained from analysis of variance (ANOVA) followed by Dunnett’s test
(*P < 0.05 and **P < 0.01). (C) Heat map of fold changes (expressed in
log[2] scale as indicated in the inset) in the top 1000 significantly
differentially expressed genes (Benjamini-Hochberg adjusted P < 0.001)
in K562 cells (top) and MV4;11 cells (bottom) after 6 hours of
treatment with selective BRD inhibitors or DMSO. The effects of JQ1 and
BSP are very similar and much stronger than the effects of any other
compounds targeting non-BET BRDs. (D) A published set of genes
constituting a “JQ1 signature” is only attenuated by BSP and JQ1 in
K562 cells (left) and MV4;11 cells (right) after 6 hours of treatment
with a series of inhibitors (500 nM). The heat map depicts
row-normalized values for gene expression, as indicated in the inset.
Despite the differences across cell lines, we were surprised by the
high similarity in transcription response caused by both inhibitors and
by the degree of the observed changes in gene expression (fig. S5B).
Given the small differences in individual genes, we decided to
investigate the underlying gene ontologies enriched by each inhibitor
in the four cell lines. Surprisingly, between inhibitors, there were
differences in the underlying biological processes perturbed (fig. S6)
or in the cellular components affected (fig. S7A) within the same line.
We also performed a MetaCore analysis based on manually curated
ontologies, and we found similar enrichment of pathways or process
networks between inhibitors for the same line (fig. S7B and table S2).
We next performed gene set enrichment analysis (GSEA), seeking to
identify enrichment of similar functions between the four leukemia
lines. BSP-treated cells enriched several signatures from the Molecular
Signatures Database (MSigDB), including a very strong association with
signatures suggesting c-Myc down-regulation (figs. S8 and S9).
BSP exhibits a strong BET-like signature in leukemia
We were intrigued by the strong association with c-Myc down-regulation
signatures uncovered by GSEA. c-Myc transcriptional down-regulation has
been recognized as a dominant hallmark of BET inhibition in many
different tumor types ([124]24), and c-Myc reexpression has been
recently linked to BET inhibitor resistance ([125]25, [126]26). Zuber
et al. ([127]6) reported strong transcriptional attenuation of the AML
cell line THP1 after a 24-hour treatment with JQ1. With these data, we
constructed a gene set signature using genes that were strongly
down-regulated in THP1 cells (P < 0.001 and fold change < −4) and
interrogated our four cell lines with GSEA. Both BSP and JQ1 elicited a
strong response and enriched this THP1 signature in all four cell lines
(fig. S10, A to D), suggesting that the effect of the transcriptional
response conferred by BSP occurs through BET BRDs. To further test
this, we explored a small gene set that was previously reported to be
regulated by JQ1 in neuroblastoma, multiple myeloma, and AML ([128]27),
and we found strong enrichment with BSP and JQ1 in all four cell lines
(fig. S10, E to H). Our data therefore suggest a dominant
transcriptional effect through BET proteins, despite the promiscuous
targeting of several diverse BRD families by BSP.
Because BSP inhibits a large number of BRD-containing proteins, we next
addressed the relative expression level of these proteins in the cell
lines studied. We were surprised to find that most BSP target proteins
were expressed across all four cell lines (fig. S11A). We would expect
BSP to bind to these proteins, reducing the effective concentration of
available BSP in the cells. We therefore investigated the effects of
non-BET BRD inhibitors specific for family III [CREBBP/EP300 using
I-CBP112 ([129]28)], family IV [BRD7/BRD9 using LP99 ([130]11) and
BRPF1/BRPF3 using OF1 ([131]12)], and family V [BAZ2A/BAZ2B using
GSK2801 ([132]9)] BRD-containing proteins that are also targeted by BSP
(fig. S11B). We performed genome-wide expression analysis in the
pan-BET inhibitor–sensitive line MV4;11 and in the less sensitive line
K562, focusing on the same initial response window of 8 hours. We
observed a striking resemblance between BSP and JQ1 compared to all
other inhibitors tested, with a clear separation of transcriptional
responses in both lines ([133]Fig. 4C). As expected, most of the
significant genes attenuated (with P < 0.001 and fold change > 1.5)
were due to BET BRD inhibition by JQ1; however, a very small subset
remained unique to BSP and did not overlap with any of the other
inhibitors, suggesting that another BSP target or synergistic
inhibition of BETs, in combination with other BRD targets, was
responsible for this population. This was also true for some
significant genes (P < 0.001), with smaller fold changes that did not
overlap with transcription responses observed using other BRD
inhibitors (fig. S12, A and B). We also noticed that attenuation of the
most significantly regulated genes was systematically different between
BSP and JQ1 and all other inhibitor classes (fig. S13A). At the gene
level, there was a small overlap between inhibitor classes (fig. S13, B
and C); however, none of the non-BET inhibitors resulted in attenuation
of the previously reported JQ1 signature, which persisted between
tissue types ([134]27) ([135]Fig. 4D). To assess whether BSP and JQ1
would act synergistically, we performed a cell toxicity study in which
eight concentrations of each inhibitor were systematically combined in
the BET inhibitor–sensitive (MV4;11) and less sensitive (K562) cell
lines (fig. S13D). This study showed that when both inhibitors were
combined at concentrations significantly lower than the individual
median effective concentration (EC[50]) values of the single compounds,
survival of both cell lines was increased, whereas at concentrations
slightly below each individual EC[50] value, we observed synergistic
effects of cellular toxicity (fig. S13E).
To obtain insights into time-dependent changes in gene expression
caused by pan-BET inhibition, we analyzed previously published data on
sensitive AML cells treated with JQ1 to define the transcriptional
response conferred by BETs after a long (24 hours) treatment. We
performed GSEA against all oncogenic signatures (MSigDB c6-gene set)
and constructed distinct directional networks for JQ1-treated THP1
cells, resulting in a complex network of signatures (fig. S14A). GSEA
followed by distinct directional network construction of BSP-induced
signatures revealed remarkable overlap with many of the late-response
JQ1-induced signatures observed in THP1 cells, but also significant
enrichment of gene sets associated with early response to BET
inhibition, such as down-regulation of the epidermal growth factor
receptor/mitogen-activated protein kinase pathway ([136]Fig. 4, B and
C), whereas longer exposure was characterized by transcriptional
regulation of key transcription factors of the E2F and HOX families.
DISCUSSION
The study presented here revealed a dominant function of BET BRDs
regulating gene transcription, compared to other selective BRD
inhibitors or the developed promiscuous BRD inhibitor BSP. Thus,
inhibition of non-BET BRDs is not likely to affect short-term gene
expression, at least in the cellular systems studied here. Selective
inhibitors for the BRDs present in SMARCA4/PB1 showed no significant
effects on gene expression but significantly contributed to the
regulation of cell-specific gene expression programs during cellular
differentiation of trophoblasts and stem cells ([137]12). Similarly,
inhibition of CREBBP/EP300 BRDs specifically affected differentiation
of leukemia-initiating cells after prolonged exposure ([138]28). These
data suggest that non-BET BRDs may be required for the organization of
chromatin structure during cell differentiation. In combination with
genome-editing techniques, chemical tools (such as BSP) that target
multiple BRDs can help rapidly establish key potential BRD targets
suitable for drug development. A recent study demonstrated how CRISPR
(clustered regularly interspaced short palindromic repeats)/CAS9 can be
used to identify BRD4 as an important drug target that sustains murine
AML cells ([139]29). BSP represents a versatile promiscuous pan-BRD
inhibitor class that has limited off-target effects (fig. S15 and table
S3) and can be used as a front-line tool to interrogate the role of the
acetylation-dependent reading process in cellular systems, leading to
similar observations compared to genetic approaches. Therefore, BSP is
a valuable tool for the identification of BRD-dependent cellular
processes, helping accelerate cell-based studies in this emerging
target area.
MATERIALS AND METHODS
Synthesis of BSP
BSP [ethyl
(3-methyl-6-(4-methyl-3-(methylsulfonamido)phenyl)-[1,2,4]triazolo[4,3-
b]pyridazin-8-yl)carbamate] was prepared according to [140]Scheme 1.
Scheme 1. Synthesis of BSP.
Scheme 1
[141]Open in a new tab
Intermediate materials were prepared as follows:
3,6-Dichloropyridazin-4-amine (2): A stainless steel reaction vessel
was charged with 3,4,6-trichloropyridazine (1) (1 g, 5.46 mmol) and
absolute ethanol saturated with ammonia (40 ml) at 0°C. The vessel was
sealed, and the mixture was heated at 125°C for 7 hours. The reaction
mixture was evaporated to dryness, and the crude product was
recrystallized from water to yield 2 (547 mg, 61%). MS [electrospray
ionization (ESI)]: mass/charge ratio (m/z) for
[C[4]H[3]Cl[2]N[3]+H]^+—calculated, 164 (2× ^35Cl), 166 (^35Cl/^37Cl),
and 168 (2× ^37Cl); found, 163.9, 165.9, and 167.8.
6-Chloro-3-hydrazinylpyridazin-4-amine (3): Dichloride 2 (547 mg, 3.33
mmol) and hydrazine (10 ml) were heated to reflux for 3 hours. The
mixture was cooled to room temperature, and water (5 ml) was added. The
resulting crystalline precipitate was collected, washed with cold
water, and dried under reduced pressure to obtain 3 (210 mg, 40%). MS
(ESI): m/z for [C[4]H[6]ClN[5]+H]^+—calculated, 160 (^35Cl) and 162
(^37Cl); found, 159.9 and 161.9.
6-Chloro-3-methyl-[1,2,4]triazolo[4,3-b]pyridazin-8-amine (4):
Hydrazine 3 (210 mg, 1.32 mmol) and acetic acid (3 ml) were heated to
reflux for 5 hours. Ice water was added, and the resulting precipitate
was collected by filtration. Recrystallization of the crude product
from methanol afforded the desired 4 (200 mg, 82%). MS (ESI): m/z for
[C[6]H[6]ClN[5]+H]^+—calculated, 184 (^35Cl) and 186 (^37Cl); found,
184.1 and 186.1.
tert-Butyl
(6-chloro-3-methyl-[1,2,4]triazolo[4,3-b]pyridazin-8-yl)carbamate (5):
Boc[2]O (7.1 g, 32.9 mol) and 4-dimethylaminopyridine (DMAP) were added
to a solution of amine 4 (2 g, 10.9 mmol) in tetrahydrofuran (THF) (60
ml) at 0°C. The mixture was allowed to warm to room temperature, and
the reaction progress was monitored by thin-layer chromatography (TLC).
Upon completion, the mixture was concentrated under reduced pressure,
and the resulting solution was diluted with EtOAc, washed with brine,
and dried (Na[2]SO[4]). The solvent was removed in vacuo, and the
residue was purified by flash column chromatography (petroleum
ether/EtOAc, 1:2) to yield 5 (1.2 g, 40%). MS (ESI): m/z for
[C[11]H[14]ClN[5]O[2]+H]^+—calculated, 284.1 (^35Cl) and 286.1 (^37Cl);
found, 284.1 and 285.0.
tert-Butyl
(3-methyl-6-(4-methyl-3-nitrophenyl)-[1,2,4]triazolo[4,3-b]pyridazin-8-
yl)carbamate (7): Pd(PPh[3])[4] (61 mg, 10% eq) and K[2]CO[3] (182 mg,
1.32 mmol) were added to a mixture of chloride 5 (150 mg, 0.53 mmol)
and boronic acid 6 (287 mg, 1.59 mmol) in dioxane/water (5.5 ml, 10:1
v/v), and the resulting mixture was heated at 120°C under Ar. The
reaction was monitored by TLC. Upon completion, water was added, and
the combined aqueous layers were extracted with dichloromethane (DCM).
The organic layers were combined and dried (Na[2]SO[4]). The solvent
was removed in vacuo, and the residue was purified by flash column
chromatography (DCM/MeOH, 50:1) to give compound 7 (90 mg, 44%). MS
(ESI): m/z for [C[18]H[20]N[6]O[4]+H]^+—calculated, 385.2; found,
385.1. ^1H nuclear magnetic resonance (NMR) (CDCl[3]): δ 8.62 (1H, d, J
= 1.8 Hz), 8.22 (1H, brs), 8.14 to 8.10 (2H, m), 7.48 (1H, d, J = 8.1
Hz), 2.88 (3H, s), 2.67 (3H, s), and 1.57 (9H, s).
3-Methyl-6-(4-methyl-3-nitrophenyl)-[1,2,4]triazolo[4,3-b]pyridazin-8-a
mine (8): Trifluoroacetic acid (2 ml) was added to a solution of
carbamate 7 (80 mg, 0.21 mmol) in DCM (10 ml), and the mixture was
stirred at room temperature. The reaction was monitored by TLC. Upon
completion, the mixture was concentrated under reduced pressure, and
the residue was purified by flash column chromatography (DCM/MeOH,
30:1) to yield 8 (64 mg, 100%). MS (ESI): m/z for
[C[13]H[12]N[6]O[2]+H]^+—calculated, 285.1; found, 285.3. ^1H NMR
[dimethyl sulfoxide (DMSO)–d[6]]: δ 8.49 (1H, d, J = 1.8 Hz), 8.19 (1H,
dd, J = 1.9 Hz, 8.0 Hz), 7.68 (1H, d, J = 8.1 Hz), 6.65 (1H, s), 2.70
(3H, s), and 2.60 (3H, s).
Ethyl
(3-methyl-6-(4-methyl-3-nitrophenyl)-[1,2,4]triazolo[4,3-b]pyridazin-8-
yl)carbamate (9): Triethylamine (0.35 ml, 2.46 mmol) was added to a
solution of amine 8 (348 mg, 1.23 mmol) in dry THF (20 ml) at 0°C.
After 30 min, ethyl chloroformate (0.34 ml, 2.46 mmol) was added, and
the reaction mixture was warmed to room temperature. The reaction was
monitored by TLC. Upon completion, water (30 ml) was added, and the
aqueous phase was extracted with DCM. The combined organic layers were
dried (Na[2]SO[4]), and the solvent was removed under reduced pressure.
The crude product was purified by flash column chromatography
(EtOAc/petroleum ether, 1:1) to yield 9 (100 mg, 23%). MS (ESI): m/z
for [C[16]H[16]N[6]O[4]+H]^+—calculated, 357.1; found, 357.1. ^1H NMR
(CDCl[3]): δ 8.60 to 8.59 (2H, m), 8.19 (1H, s), 8.10 (1H, dd, J = 1.8
Hz, 8.1 Hz), 7.52 (1H, d, J = 8.1 Hz), 4.35 (2H, q, J = 7.1 Hz), 2.85
(3H, s), 2.67 (3H, s), and 1.38 (3H, t, J = 7.1 Hz).
Ethyl
(6-(3-amino-4-methylphenyl)-3-methyl-[1,2,4]triazolo[4,3-b]pyridazin-8-
yl)carbamate (10): SnCl[2] hydrate (287 mg, 1.28 mmol) was added to a
solution of nitrobenzene 9 (91 mg, 0.26 mmol) in EtOH (10 ml). The
reaction mixture was heated to reflux and monitored by TLC. Upon
completion, the solvent was removed under reduced pressure, and the
crude product was purified by flash column chromatography (DCM/MeOH,
50:1) to yield 10 (74 mg, 87%). MS (ESI): m/z for
[C[16]H[18]N[6]O[2]+H]^+—calculated, 327.1; found, 326.9. ^1H NMR
(CDCl[3]): δ 8.36 (1H, brs), 8.13 (1H, s), 7.32 to 7.30 (2H, m), 7.16
(1H, d, J = 8.1 Hz), 4.33 (2H, q, J = 7.2 Hz), 3.80 (2H, brs), 2.84
(3H, s), 2.23 (3H, s), and 1.37 (3H, t, J = 7.2 Hz).
Ethyl
(3-methyl-6-(4-methyl-3-(methylsulfonamido)phenyl)-[1,2,4]triazolo[4,3-
b]pyridazin-8-yl)carbamate (11) (BSP): Methanesulfonyl chloride (2 eq)
was added to a solution of amine 10 (1 eq) in DCM (0.037 M), followed
by addition of pyridine (0.6 eq). The resulting mixture was stirred at
room temperature. The reaction was monitored by TLC. Upon completion,
water was added, and the aqueous layer was extracted with DCM. The
organic layers were combined and dried (Na[2]SO[4]). The solvent was
removed, and the residue was purified by flash column chromatography
(DCM/MeOH, 30:1) to give BSP 11 (78%). MS (ESI): m/z for
[C[17]H[20]N[6]O[4]S+H]^+—calculated, 405.1; found, 405.5. ^1H NMR
(CDCl[3]): δ 8.93 (1H, brs), 8.14 (1H, s), 8.10 (1H, d, J = 1.8 Hz),
7.70 (1H, dd, J = 1.8 Hz, 7.8 Hz), 7.64 (1H, brs), 7.32 (1H, d, J = 8.1
Hz), 4.30 (2H, q, J = 7.1 Hz), 3.09 (3H, s), 2.82 (3H, s), 2.44 (3H,
s), and 1.34 (3H, t, J = 7.2 Hz).
Cloning, protein expression, and purification
Human BRDs were subcloned into bacteria-expressing vectors [pNIC28-Bsa4
(GenBank [142]EF198106) and pNIC-Bio2 (GenBank [143]JF912191)],
expressed, and purified as previously described by Filippakopoulos et
al. ([144]1).
Thermal stability assay (T[m] shift)
Thermal melting experiments were carried out using an Mx3005P Real-Time
PCR machine (Stratagene). Proteins were buffered in 10 mM Hepes buffer
(pH 7.5) and 500 mM NaCl and assayed on a 96-well plate at a final
concentration of 2 μM in a volume of 20 μl. Compounds were added at a
final concentration of 10 or 100 μM. SYPRO Orange Protein Gel Stain
(Molecular Probes) was added as a fluorescence probe at a dilution of
1:1000. Excitation and emission filters for the SYPRO Orange dye were
set to 465 and 590 nm, respectively. The temperature was raised from
25° to 96°C at a step of 3°C/min, and fluorescence readings were taken
at each interval. The temperature dependence of the fluorescence during
the protein denaturation process was approximated by the equation
[MATH: y(T)=yF+yU−yF1+eΔuG(T)/RT
:MATH]
where ΔuG is the difference in unfolding free energy between the folded
state and the unfolded state, R is the gas constant, and y[F] and y[U]
are the fluorescence intensities of the probe in the presence of
completely folded and unfolded proteins, respectively ([145]30). The
baselines of the denatured and native states were approximated by a
linear fit. The observed temperature shifts (ΔT[m]^obs) were recorded
as the difference between the transition midpoints of sample and
reference wells containing proteins without ligands in the same plate
and were determined by nonlinear least-squares fit. Temperature shifts
(ΔT[m]^obs) for three independent measurements per protein/compound are
summarized in table S1 and [146]Table 2.
Biolayer interferometry
Experiments were performed on an Octet RED384 System (FortéBio) at 25°C
in 20 mM Hepes (pH 7.5), 150 mM NaCl, and 0.5 mM
tris(2-carboxyethyl)phosphine using the FortéBio data acquisition
software V.7.1.0.100. Biotinylated BRDs were immobilized onto Super
Streptavidin biosensors (Super Streptavidin Dip and Read Biosensors for
kinetic no. 18-0011; FortéBio), preequilibrated in the BLI buffer, and
quenched in a solution of 5 μM biotin (baseline equilibration for 60 s,
peptide loading for 240 s, and quenching for 60 s; shake speed of 1000
rpm at 25°C). The immobilized proteins were subsequently used in
association and dissociation measurements performed within a time
window of 600 s (baseline equilibration for 60 s, association for 600
s, and dissociation for 600 s; shake speed of 1000 rpm at 25°C).
Interference patterns from protein-coated biosensors without proteins
were used as controls. After referencing corrections, the subtracted
binding interference data were analyzed using the FortéBio data
analysis software V.7.1.0.38 (provided with the instrument) following
the manufacturer’s protocols.
Isothermal titration calorimetry
Experiments were carried out on an ITC200 microcalorimeter from
MicroCal LLC (GE Healthcare) equipped with a washing module, a reaction
cell (volume of 0.2003 ml), and a 40-μl microsyringe. Experiments were
carried out in ITC buffer [50 mM Hepes (pH 7.5; 25°C) and 150 mM NaCl]
at 15°C while stirring at 1000 rpm. The microsyringe was loaded with a
solution of a protein sample (200 to 650 μM in ITC buffer) and was
carefully inserted into the calorimetric cell, which was filled with
the compound (0.2 ml, 13 to 25 μM in ITC buffer). The system was first
allowed to equilibrate until the cell temperature reached 15°C, and an
additional delay of 60 s was applied. All titrations were conducted
using an initial control injection of 0.3 μl, followed by 38 identical
injections of 1 μl for a duration of 2 s (per injection) and with a
spacing of 120 s between injections. The titration experiments were
designed to ensure complete saturation of the proteins before the final
injection. The heats of dilution for the proteins were independent of
their concentrations and corresponded to the heats observed from the
last injection, after saturation of ligand binding, thus facilitating
estimation of the baseline of each titration from the last injection.
The collected data were corrected for protein heats of dilution
(measured in separate experiments by titrating the proteins into ITC
buffer) and deconvoluted using the MicroCal Origin software (supplied
with the instrument) to yield enthalpies of binding (ΔH) and binding
constants (K[B]), as previously described in detail by Wiseman et al.
([147]31). Thermodynamic parameters were calculated using the basic
equation of thermodynamics (ΔG = ΔH − TΔS = −RTlnK[B], where ΔG, ΔH,
and ΔS are the changes in free energy, enthalpy, and entropy of
binding, respectively). In all cases, a single binding site model was
used (supplied with the MicroCal Origin software package). Dissociation
constants and thermodynamic parameters are listed in [148]Table 1.
Crystallization
Aliquots of the purified proteins were set up for crystallization using
a mosquito crystallization robot (TTP Labtech). Coarse screens were
typically set up onto Greiner three-well plates using three different
drop ratios of precipitant to protein per condition (100 + 50, 75 + 75,
and 50 + 100 nl). Initial hits were optimized, further scaling up the
drop sizes. All crystallizations were carried out using the
sitting-drop vapor diffusion method at 4°C. BRD4(1) crystals with BSP
were grown by mixing 200 nl of the protein (9.9 mg/ml and 5 mM final
ligand concentration) with 100 nl of reservoir solution containing 0.20
M sodium/potassium tartrate, 0.1 M BT-propane (pH 8.5), 20%
polyethylene glycol (PEG) 3350, and 10% ethylene glycol. TAF1L(2)
crystals with BSP were grown by mixing 150 nl of the protein (11.2
mg/ml and 10 mM final ligand concentration) with 150 nl of reservoir
solution containing 0.1 M MMT [mixture of dl-malic acid and
2-(N-morpholino)-ethanesulfonic acid monohydrate] (pH 7.5) and 63%
2-methyl-2,4-pentanediol. BRD9 crystals with BSP were grown by mixing
100 nl of the protein (28 mg/ml and 10 mM final ligand concentration)
with 200 nl of reservoir solution containing 0.1 M bis-tris (pH 5.5),
0.2 M NaCl, and 25% PEG3350. Diffraction-quality crystals grew within a
few days.
Data collection and structure solution
BRD4(1) crystals were cryoprotected using the well solution
supplemented with additional ethylene glycol and were flash-frozen in
liquid nitrogen. Data were collected in-house on a Rigaku FRE rotating
anode system equipped with an R-AXIS IV detector at 1.52 Å
[BRD4(1)/BSP], at Diamond beamline I02 at a wavelength of 0.9795 Å
[TAF1L(2)/BSP], or at Diamond beamline I04 at a wavelength of 0.9795 Å
(BRD9/BSP). Indexing and integration were carried out using XDS
([149]32, [150]33), and scaling was performed with Scala ([151]34).
Initial phases were calculated by molecular replacement with PHASER
([152]35) using the known models of BRD4(1), TAF1L(2), or BRD9 [Protein
Data Bank (PDB) accession codes 2OSS, 3HMH, and 3HME, respectively].
Initial models were built by ARP/wARP ([153]36) followed by manual
building in Coot ([154]37). Refinement was carried out in REFMAC5
([155]38). Thermal motions were analyzed using TLS Motion Determination
([156]39), and hydrogen atoms were included in late refinement cycles.
Data collection and refinement statistics are found in table S4. The
model and structure factors have been deposited with PDB accession
codes 5IGK [BRD4(1)/BSP], 5IGL [TAF1L(2)/BSP], and 5IGM (BRD9/BSP).
BSP pull-down assay
BSP pull-downs were performed using two different biotinylated probes
(BSP-a and BSP-b) from a lysate of ~2 × 10^7 HEK293T cells per sample.
Briefly, to the frozen HEK293T cell pellet, 1.6 ml of ice-cold lysis
buffer [50 mM Hepes-NaOH (pH 8.0), 2 mM EDTA, 0.1% NP-40, 10% glycerol,
1 mM phenylmethylsulfonyl fluoride, 1 mM dithiothreitol, and Sigma
Protease Inhibitor Cocktail (P8340, 1:500) with 300 mM KCl] was added
per 15-cm plate of cells, and the frozen pellet was gently resuspended.
Samples were subjected to a freeze/thaw cycle on dry ice until
completely frozen (5 to 10 min) and then transferred to a 37°C water
bath with agitation until only a small amount of ice remained. Samples
were sonicated for 30 s (10 s on–2 s off cycles at an amplitude of
0.35) using a Qsonica 125-W sonicator equipped with a ^1/[8]-inch probe
to shear DNA. Benzonase (1 μl, 250 U/μl; E1014; Sigma-Aldrich) was then
added to each sample and incubated at 4°C for 1 hour to further digest
chromatin. The resulting samples were centrifuged at 14,000 rpm
(20,873g) for 20 min at 4°C, and the supernatant was transferred to
fresh 2-ml tubes. Biotinylated BSP probes [50 nmol conjugated to 20 μl
of MyOne Streptavidin C1 Dynabeads (65002; Invitrogen) for at least an
hour in 1× phosphate-buffered saline (PBS)] were washed with lysis
buffer, and an equal bead volume was subsequently aliquoted between
centrifuged cell lysates. The mixture was incubated for 2 hours at 4°C
with gentle agitation (nutator) with or without competition from 30
nmol of BSP. Beads were then pelleted by centrifugation (1000 rpm for 5
s), and tubes were placed on a cold magnetic rack (on ice) to collect
the beads on the side of the tubes. The supernatant was removed slowly
with a pipette, and the beads were washed once with 1 ml of cold lysis
buffer with 300 mM KCl and washed twice with lysis buffer containing
100 mM KCl. The beads were then transferred to a fresh 1.7-ml tube with
1 ml of 20 mM tris-HCl (pH 8.0) and 2 mM CaCl[2]. After the last wash,
the samples were quickly centrifuged, and the last drops of liquid were
removed with a fine pipette.
Binding of BSP to CECR2 was evaluated in Flp-In T-REx HEK293 cells
stably expressing 3×FLAG CECR2 (accession no. [157]BC166664) or an
empty 3×FLAG control (using lysis buffer containing 300 mM KCl). After
pull-down, proteins were eluted off the beads by adding 40 μl of 2×
Laemmli buffer and heating the samples to 65°C for 15 min. The samples
were then cooled to room temperature, quickly centrifuged, and placed
on a magnetic rack to collect the beads on the side of the tubes. The
supernatants were then transferred to fresh tubes and stored at −40°C
until Western blots were performed. One percent of input and 25% of
purified proteins were separated by SDS–polyacrylamide gel
electrophoresis and transferred onto nitrocellulose membranes. The
membranes were blocked in tris-buffered saline containing nonfat milk
(5 mg/ml) and 1% Tween 20 for 1 hour at room temperature. Blots were
probed for FLAG (1:5000; F1804; Sigma-Aldrich) or β-tubulin (1:5000;
E7; Developmental Studies Hybridoma Bank) at the University of Iowa.
Detection on film was performed by chemiluminescence using the LumiGLO
reagent (1:20; 7003; Cell Signaling Technology).
Trypsin digestion of affinity-purified proteins
After pull-down on magnetic beads, samples were resuspended in 7.5 μl
of 20 mM tris-HCl (pH 8.0) containing 500 ng of trypsin (Trypsin
Singles, T7575; Sigma-Aldrich), and the suspension was incubated at
37°C with agitation overnight on an angled rotating wheel (~15 hours).
After this first incubation, samples were quickly centrifuged and then
magnetized, and the supernatants were transferred to a fresh tube.
Another 250 ng of trypsin was added [in 2.5 μl of 20 mM tris-HCl (pH
8.0)], and the resulting sample was incubated at 37°C for 3 to 4 hours
without agitation. Formic acid was then added to a final concentration
of 2% (from 50% stock solution) and stored at −80°C.
MS analysis
Pull-down samples and controls were analyzed by MS, as previously
described by Barsyte-Lovejoy et al. ([158]40). Briefly, 5 μl of each
sample (representing ~50% of the sample) was directly loaded at a flow
rate of 400 nl/min onto a 75 μm × 12 cm emitter packed with a 3-μm
ReproSil-Pur C18-AQ (Dr. Maisch GmbH HPLC). The peptides were eluted
from the column over a 90-min gradient generated by a NanoLC-Ultra 1D
Plus nanopump (Eksigent) and analyzed on a TripleTOF 5600 Instrument
(AB SCIEX). The gradient was delivered at a flow rate of 200 nl/min
starting from 2% acetonitrile with 0.1% formic acid, ramping up to 35%
acetonitrile with 0.1% formic acid over 90 min, followed by a 15-min
cleanup at 80% acetonitrile with 0.1% formic acid and a 15-min
equilibration period back to 2% acetonitrile with 0.1% formic acid, for
a total of 120 min. To minimize carryover between each sample, we
washed the analytical column for 3 hours by running an alternating
sawtooth gradient from 35% acetonitrile with 0.1% formic acid to 80%
acetonitrile with 0.1% formic acid, holding each gradient concentration
for 5 min. Analytical column and instrument performance were verified
after each sample by loading 30 fmol of bovine serum albumin (BSA)
tryptic peptide standard (Michrom Bioresources Inc.) with 60 fmol of
α-casein tryptic digest and by running a short 30-min gradient.
Time-of-flight (TOF) MS calibration was performed on BSA reference ions
before the next sample was run to adjust for mass drift and to verify
peak intensity. The instrument method was set to discovery or
information-dependent acquisition mode, which consisted of one 250-ms
TOF MS1 survey scan from 400 to 1300 Da, followed by twenty 100-ms MS2
candidate ion scans from 100 to 2000 Da in high-sensitivity mode. Only
ions with charges of 2+ to 4+, which exceeded a threshold of 200 cps,
were selected for MS2, and former precursors were excluded for 10 s
after one occurrence.
MS data analysis
MS data generated by TripleTOF 5600 were stored, searched, and analyzed
using the ProHits laboratory information management system platform
([159]41). Within ProHits, the resulting WIFF files were first
converted into an MGF format using the WIFF2MGF converter and into an
mzML format using ProteoWizard (v3.0.4468) and the AB SCIEX MS Data
Converter (V1.3 beta) and then searched using Mascot (v2.3.02) and
Comet (v2012.02 rev.0). The spectra were searched with the RefSeq
database (version 53; 28 May 2014) acquired from the National Center
for Biotechnology Information (NCBI) against a total of 34,374 human
and adenovirus sequences supplemented with “common contaminants” from
the Max Planck Institute
([160]http://141.61.102.106:8080/share.cgi?ssid=0f2gfuB) and the Global
Proteome Machine ([161]www.thegpm.org/crap/index.html). The database
parameters were set to search for tryptic cleavages, allowing up to two
missed cleavage sites per peptide, with a mass tolerance of 40 parts
per million for precursors with charges of 2+ to 4+ and a tolerance of
±0.15 atomic mass units for fragment ions. Variable modifications were
selected for deamidated asparagine and glutamine and for oxidized
methionine. The results from each search engine were analyzed through
TPP [the Trans-Proteomic Pipeline ([162]42) v4.6 OCCUPY rev 3] by means
of the iProphet pipeline ([163]43). The resulting MS data were
presented in a bar graph displaying the ratio of spectral counts
obtained for every BRD-containing protein in the presence and absence
of competing nonbiotinylated BSP. All MS files used in this study were
also deposited at MassIVE ([164]http://massive.ucsd.edu) under MassIVE
ID MSV000079365.
Cell culture
Human cell lines (K562, KASUMI-1, and MV4;11) ([165]44–[166]46) were
obtained from the American Type Culture Collection and the Leibnitz
Institute Deutsche Sammlung von Mikroorganismen und Zellkulturen
(German Collection of Microorganisms and Cell Cultures)
([167]www.dsmz.de). Cell lines were cultured in RPMI 1640 medium
(catalog no. 61870-044; Gibco) containing 10% fetal calf serum (catalog
no. 2-01F10-I; BioConcept), penicillin (100 U/ml), and streptomycin
(100 U/ml) (catalog no. 15140-122; Gibco). The OCI-AML3 cell line
([168]47) was maintained in α minimum essential medium (catalog no.
BE12-169F; BioWhittaker) supplemented with 20% heat-inactivated fetal
calf serum (no. A15-152; PAA). HEK293T cells were grown in Dulbecco’s
modified Eagle’s medium supplemented with 5% fetal bovine serum
(catalog no. 12483-020; Gibco), 5% cosmic calf serum (catalog no.
SH30087.03; HyClone), and penicillin-streptomycin (catalog no.
30-002-CI; Corning). Cells were grown at 37°C in a humidified cabinet
under 5% CO[2] (Heraeus Function Line).
In vitro cytotoxicity assays
Cytotoxic activity of BSP on leukemic cell lines was assessed using two
different colorimetric assays. Cell viability was assessed using Trypan
blue (Sigma). Cells were harvested from exponential phase cultures and
plated on 96-well opaque flat-bottom plates at a cell density of 4 ×
10^4 cells per well (50 μl). After 2 to 4 hours of recovery, 50 μl of a
medium containing DMSO (vehicle) or the test compound was added to the
wells. For each concentration, cells were plated in quadruplicate.
Cells were exposed to the compound for 48 and 72 hours before 10 μl of
WST-1 reagent (catalog no. 05015944001; Roche) was added to every well.
After 30 s on an orbital shaker and further incubation for 2 hours,
absorbance of the samples was measured with an enzyme-linked
immunosorbent assay (ELISA) plate reader (Synergy H1 Hybrid Multi-Mode
Microplate Reader) at a wavelength of 450 versus 650 nm (background).
Samples were blanked with a control well, and the percentage of
surviving cells were compared to controls (fig. S3). Cytotoxic activity
of JQ1 and BSP on leukemic cell lines was also assessed using the
colorimetric CellTiter Aqueous Non-Radioactive Cell Proliferation Assay
(Promega). Cell viability was assessed using Trypan blue (Sigma). Cells
were harvested from exponential phase cultures and plated on 96-well
opaque flat-bottom plates at a cell density of 2 × 10^5 cells per well
(100 μl). After 2 to 4 hours of recovery, 100 μl of a medium containing
DMSO (vehicle) or the test compound was added to the wells. For each
concentration, cells were plated in triplicate. Cells were exposed to
the compound for 72 hours before the addition of 40 μl of
3-(4,5-dimethylthiazol-2-yl)-5-(3-carboxymethoxyphenyl)-2-(4-sulfopheny
l)-2H-tetrazolium (MTS), in the presence of phenazine methosulfate
(PMS) to each well. After 30 s on an orbital shaker and further
incubation for 2 hours, absorbance of the samples at 485 nm was
measured with an ELISA plate reader (PHERAstar; BMG LABTECH) (fig.
S4A).
NCI-60 growth inhibition determination
BSP was submitted to the NCI Human Tumor Cell Line Screen
([169]https://dtp.cancer.gov/discovery_development/nci-60/) and
profiled against 60 tumor cell lines, first at a single dose of 10 μM
and then in a serial dilution of five concentrations, following the NCI
standard screening protocol ([170]48).
Binding against human recombinant ligands and ion receptors (CEREP)
Selectivity profiling (ExpresSProfile) was performed on BSP against 104
ligand receptors, ion channels, and transport proteins by CEREP using
the manufacturer’s protocols. Data were analyzed in Microsoft Excel
(Microsoft Corp.).
Clonogenic and replating assays in methylcellulose
The impact of BSP on the clonogenic potential of cells was assessed in
methylcellulose cultures at different concentrations (0.1, 0.5, and 1.0
μM) or in DMSO (vehicle control). KASUMI-1 and MV4;11 cells were plated
in methylcellulose supplemented with human cytokines (MethoCult H4535;
STEMCELL Technologies), whereas K562 and MV4;11 cells were plated in
methylcellulose without additional cytokines (5510; StemAlpha) at 2 ×
10^3 cells per plate. All plates were incubated at 37°C and 5% CO[2]
for 6 to 10 days before the number of colonies was counted and before
viable cells were harvested. Cytospots were prepared by centrifuging
10^5 cells at 300 rpm for 3 min using a Shandon Cytospin 3 centrifuge.
Cytospots were stained with Wright-Giemsa stain and analyzed with an
Olympus BX62 or Nikon TI microscope at ×60 magnification.
Cell cycle analysis
Cells were treated in liquid culture with increasing concentrations of
BSP or JQ1. After 48 hours, cells were washed twice with PBS, fixed
with ice-cold 70% ethanol, and stained in a solution containing
propidium iodide (50 μg/ml; P4684; Sigma), ribonuclease (10 μg/ml;
10109142001; Roche), and 1% Triton X-100 in PBS. DNA content was
measured on an Accuri C6 cytometer (BD Biosciences), and data were
analyzed using the FlowJo software suite (TreeStar Inc.). Experiments
were repeated three times.
Inhibitor combination analysis
Cell lines (MV4;11 and K562 from the log growth phase in RPMI–10% fetal
calf serum) were treated with inhibitors (JQ1 or BSP) individually or
in combination [JQ1: 0.7 to 11,392 nM (15× concentration); BSP: 37 to
4728 nM (8× concentration)]. Cell survival was determined using a WST-1
assay (no. 05015944001; Roche). Raw data were blanked and normalized to
DMSO-treated controls. Data sets were log-transformed and normalized
individually, and the combination index ([171]49) was calculated for
each inhibitor combination using the CompuSyn software suite (ComboSyn
Inc.).
Fluorescence recovery after photobleaching
FRAP studies were performed using a protocol previously described by
Philpott et al. ([172]50). In brief, U2OS cells were transfected
(Lipofectamine; Invitrogen) with mammalian overexpression constructs
encoding GFP chimeras with BRD4 or BRD9. The FRAP and imaging system
consisted of a Zeiss LSM 710 scanhead (Zeiss GmbH) coupled to an
inverted Zeiss Axio Observer.Z1 microscope equipped with a
high–numerical aperture (1.3) ×40 oil immersion objective (Zeiss GmbH)
equipped with a heated chamber set to 37°C. FRAP and GFP fluorescence
imaging were carried out with an argon-ion laser (488 nm) and with a
piezomultiplier tube detector set to detect fluorescence between 500
and 550 nm. A 5-μm^2 region of the nucleus was selected, and the region
was bleached after five prescans. A time-lapse series was then taken to
record GFP recovery using 1% of the power used for bleaching at an
interval of 0.25 s. The image data sets and fluorescence recovery data
were exported from the ZEN 2010 microscope control software into Origin
v.7. The average intensity at each imaging time point was measured for
three regions of interest: the bleached region (I[t]), the total cell
nucleus (T[t]), and a random region outside the cell for background
subtraction (BG). The relative fluorescence signal in the bleached
region was calculated for each time point t, with the following formula
([173]51)
[MATH: (Taverage
prebleach−BG)(It−BG)/(Tt−BG)(Iaverage
prebleach−BG) :MATH]
The baseline was normalized to zero, and the prebleach was normalized
to 1. Half times of recovery were calculated from the individual curves
and presented as the mean. Paired t tests were used to generate P
values for comparisons between two groups.
RNA extraction
Cells were seeded at 2 × 10^5 cells/ml on the day before treatment.
Treatments were performed so that a final concentration of 0.1% DMSO
(catalog no. D1435; Sigma) was achieved, and cells were incubated with
the vehicle or test compound for 6 hours before isolation of RNA. Total
RNA was isolated using a standard TRIzol (Invitrogen) protocol and
prepared using RNeasy columns (catalog no. 74106 plus; Qiagen). RNA was
quantified using a NanoDrop spectrophotometer (model ND1000; Thermo
Fisher Scientific), and integrity was assessed on a BioAnalyzer (2100;
Agilent Laboratories). All samples had an RNA integrity number of ≥9.
Genome-wide expression analysis
mRNA samples were processed using the Illumina TotalPrep-96 RNA
Amplification Kit followed by the Illumina Whole-Genome Gene Expression
Direct Hybridization Assay. The labeled complementary RNA was then
hybridized on Illumina HumanHT-12 v4 bead chips (Illumina Inc.). Chips
were processed on an Illumina iScan Scanner, and the Illumina
GenomeStudio (v.1.9.0; Illumina Inc.) was used to generate bead files.
GenomeStudio data were processed in R (v.3.2) ([174]52) using
Bioconductor (v.3.1) ([175]53) and the lumi package (v.2.20.2)
([176]54). Quality controls were carried out using the
arrayQualityMetrics package (v.3.24.0) ([177]55), taking into account
array intensity distributions, distance between arrays, and variance
mean dependence. Principal components analysis was used to decide which
arrays to process together. Background correction followed by
variance-stabilizing transform ([178]56) and quantiles between
microarrays normalization were carried out with the lumi package. From
the 47,231 probe sets available on the HumanHT12 V4 chip, removal of
unexpressed probes resulted in 24,283 probe sets. A linear model was
applied using the limma package (v.3.24.13) ([179]57), followed by
empirical Bayesian analysis, to determine differential expression
between untreated and treated samples. Genes were considered to be
differentially expressed if the adjusted P value [calculated using the
Benjamini-Hochberg method ([180]58) to minimize false discovery rate
(FDR)] was less than 0.05 and the mean level of expression was greater
than 1.5-fold. Gene Ontology (GO) enrichment analysis was performed
with the topGO package (v.2.20.0) ([181]59) using the weight01
algorithm and Fisher’s exact test to calculate the significance of a GO
term. A cutoff value of 0.01 was imposed on the adjusted P values to
report enriched terms. Genes exhibiting a differential expression upon
BSP or JQ1 treatment (Benjamini-Hochberg adjusted P < 0.01) were
further subjected to enrichment analyses in the MetaCore software suite
(MetaCore v.6.19.65960; Thomson Reuters) to reveal signaling and
metabolic pathways, as well as cell process networks overrepresented in
the differentially expressed gene sets. P values for pathway enrichment
analysis were calculated using the formula for hypergeometric
distribution, reflecting the probability for a pathway to arise by
chance. Statistically enriched pathways and networks were identified
using a threshold FDR of 0.001. Gene expression data have been
deposited in NCBI’s Gene Expression Omnibus and are accessible through
GEO Series accession number [182]GSE78830.
Gene set analysis and GSEA
Gene expression data were further filtered to remove unannotated genes,
resulting in 18,754 probes. Multiprobe profiles were averaged using the
collapseRows R function ([183]60), resulting in 13,620 unique genes,
which were imported into the Broad GSEA suite (v.2.2.0) ([184]61) as a
collapsed set. Gene set analysis was performed with the piano package
(v.1.8.2) ([185]62) using the MaxMean method ([186]63), with 1000
permutations and with minimum and maximum gene sets of 15 and 500,
respectively, against the 50 hallmark (h) gene sets from the MSigDB
(v.5.0). Resulting gene sets with a nominal P value of 0.05 were
considered significant. Distinct nondirectional and directional network
maps were visualized with the piano package.
GSEA was performed with the Broad GSEA suite (v.2.2.0) ([187]61) in a
collection of 4725 curated gene sets (c2), 615 transcription factors
(c3), and 50 hallmarks (h) from MSigDB (v.5.0). Gene sets with less
than 15 genes or more than 500 genes were excluded from the analysis,
whereas gene sets with an FDR of ≤0.25 and a nominal P value of ≤0.05
were considered significant. Gene ranking was performed with the
weighted enrichment score using a two-sided signal-to-noise ratio, and
P values were calculated using 1000 permutations of the gene set.
JQ1 gene signatures
The AML JQ1 signature was constructed using the gene expression data
(GEO data set: [188]GSE29799) after a 24-hour treatment of THP1 cells
with 250 nM JQ1, as reported by Zuber et al. ([189]6). Differentially
expressed genes with an adjusted P value of <0.001 and a log fold
change of >2, which were down-regulated (185 genes), were used as a
ranked list to construct a gene set for subsequent GSEA. A smaller JQ1
signature consisting of 36 genes previously reported by Puissant et al.
([190]27) to be down-regulated by JQ1 in neuroblastoma, multiple
myeloma, and AML was also used to construct a gene set for subsequent
GSEA.
Quantitative real-time polymerase chain reaction
One microgram of RNA was used to prepare cDNA using the iScript cDNA
synthesis kit (catalog no. 1708891; Bio-Rad) according to the
manufacturer’s instructions. The resulting cDNA was diluted 1:10 before
qRT-PCR was performed. Samples were prepared on 384-well plates with a
final reaction volume of 10 μl containing SYBR Select Master Mix
(catalog no. 4472908; Thermo Fisher Scientific), 0.4 μM forward and
reverse primers, and 2 μl of diluted cDNA. All reactions were run on a
QuantStudio 6 Flex Real-Time PCR system (Thermo Fisher Scientific)
under the following conditions: 1 cycle at 50°C for 2 min and then 1
cycle at 95°C for 2 min, followed by 40 cycles at 95°C for 15 s and
then 60°C for 1 min each. Data were analyzed using the 2^−ΔΔCT method
([191]64), and sample normalization was performed using 18S ribosomal
RNA as the endogenous control and SDHA as the reference gene. Values
are presented as means ± SD from three biological replicates. P values
are presented such that ****P < 0.001, ***P < 0.005, **P < 0.01, and *P
< 0.05 and were evaluated with one-step ANOVA followed by Dunnett’s
test (performed in GraphPad Prism v.6). Primers are listed in table S5.
Supplementary Material
http://advances.sciencemag.org/cgi/content/full/2/10/e1600760/DC1
[192]supp_2_10_e1600760__index.html^ (2.9KB, html)
Acknowledgments