1
Epigenetic regulation of the PTEN-AKT-RAC1 axis by G9a is critical for tumor growth in
alveolar rhabdomyosarcoma
Akshay V Bhat1 , Monica Palanichamy Kala
1, Vinay Kumar Rao
1, Luca Pignata
2, Huey Jin Lim
3,
Sudha Suriyamurthy1, Kenneth T Chang
4, Victor K Lee
3, Ernesto Guccione
2, and Reshma Taneja
1*
1 Department of Physiology, Yong Loo Lin School of Medicine, National University of Singapore,
Singapore 117593
2 Institute of Molecular and Cell Biology (IMCB), Agency for Science, Technology and Research
(A*STAR), 61 Biopolis Drive, Proteos Building #03-06, 138673 Singapore
3 Department of Pathology, Yong Loo Lin School of Medicine, National University of Singapore,
Singapore 117597
4 Department
of Pathology, KK Women and Children’s Hospital, Singapore 229899
Running Title: G9a regulates RAC1 activity in ARMS
*Author for correspondence
Email: [email protected]
Tel +65 6516 3236
Fax: +65 6778 8161
Disclosure of Potential Conflicts of Interest: The authors declare no potential conflicts of interest
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Abstract
Alveolar Rhabdomyosarcoma (ARMS) is an aggressive pediatric cancer with poor prognosis. As
transient and stable modifications to chromatin have emerged as critical mechanisms in oncogenic
signaling, efforts to target epigenetic modifiers as a therapeutic strategy have accelerated in recent
years. To identify chromatin modifiers that sustain tumor growth, we performed an epigenetic screen
and found that inhibition of lysine methyltransferase G9a significantly impacted the viability of ARMS
cell lines. Targeting expression or activity of G9a reduced cellular proliferation and motility in vitro and
tumor growth in vivo. Transcriptome and chromatin immunoprecipitation-sequencing analysis
provided mechanistic evidence that the tumor suppressor PTEN was a direct target gene of G9a. G9a
repressed PTEN expression in a methyltransferase activity-dependent manner, resulting in increased
AKT and RAC1 activity. Re-expression of constitutively active RAC1 in G9a-deficient tumor cells
restored oncogenic phenotypes, demonstrating its critical functions downstream of G9a. Collectively,
our study provides evidence for a G9a-dependent epigenetic program that regulates tumor growth
and suggests targeting G9a as a therapeutic strategy in ARMS.
Significance: Findings demonstrate that RAC1 is an effector of G9a oncogenic functions and
highlight the potential of G9a inhibitors in the treatment of ARMS.
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Introduction
Rhabdomyosarcoma (RMS) is a highly malignant soft tissue sarcoma of childhood which accounts for
approximately 5-8% of paediatric cancers occurring mostly in children below 10 years of age. Despite
expression of the myogenic proteins MyoD and myogenin, RMS cells fail to undergo myogenic
differentiation and are morphologically similar to premature mesenchymal cells (1). The most common
forms of RMS are Embryonal Rhabdomyosarcoma (ERMS) and Alveolar Rhabdomyosarcoma
(ARMS). ARMS, the most aggressive subtype, is associated with frequent metastasis at the time of
diagnosis and exhibits limited response to treatment resulting in poor survival rates. ARMS is
distinguished from the other subtypes of RMS by frequent translocations t(2; 13) (q35; q14) and t(1;
13) (p36; q14) resulting in the expression of PAX3-FOXO1 and PAX7-FOXO1 fusion proteins in 60%
and 20% cases respectively (2,3). PAX3-FOXO1 is believed to be critical for oncogenesis by control
of genes required for proliferation, survival and metastasis (4-8). ARMS expressing either of these
translocations are termed fusion positive, whereas a small percentage of ARMS tumors (~20%) are
devoid of these chromosomal translocations and are termed fusion negative. Fusion negative ARMS
are similar to ERMS in terms of prognosis, event-free survival, frequency of metastases, and
distribution of site signifying the importance of PAX3/PAX7-FOXO1 in fusion positive ARMS tumors
(9). In addition to the presence of fusion oncoproteins, comprehensive analysis of the genomic
landscape in ARMS has shown de-regulation of growth factor signaling including IGF/IGF1R,
mutations in PIK3CA and amplification of CDK4 (10-12). Elevated growth factor levels stimulate the
PI3K pathway resulting in phosphorylation of phosphatidylinositol-4,5-bisphosphate [PIP2] to produce
phosphatidylinositol-3,4,5-bisphosphate [PIP3]. This facilitates recruitment of AKT to the plasma
membrane. Subsequent phosphorylation of AKT at threonine 308 (Thr308) by phosphoinositide-
dependent kinase (PDK1), and at serine 473 (Ser473) by the mammalian target of rapamycin
complex (mTORC)-2 leads to its activation and impacts several cellular processes such as
proliferation, growth, survival, migration and metabolism (13). The guanine nucleotide exchange
factors (GEF) are also activated by PI3K/AKT signaling resulting in increased Ras-related C3
botulinum toxin substrate 1 (RAC1) activity (14). Elevated RAC1 activity is associated with aggressive
cancers (15) and has multiple effects on growth, proliferation and cell invasion. The tumor suppressor
PTEN (Phosphatase and tensin homolog) dephosphorylates PIP3 and thus negatively regulates the
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PI3K-AKT pathway. The down regulation of PTEN expression in ARMS contributes to elevated
PI3K/AKT/RAC1 signaling that is linked to tumor cell survival (16,17). However the molecular
mechanisms that result in loss of PTEN expression are not understood and could provide a tool to
suppress oncogenic signaling through the PI3K/AKT/RAC1 pathway.
Recent advances in chromatin biology have revealed de-regulation of the epigenetic landscape in
ARMS (18). The chromatin modifiers CHD4, PCAF and BRD4 have been shown to interact with and
impact PAX3-FOXO1 activity (19-21). Nevertheless, the epigenetic network that is central to ARMS
tumorigenesis remain to be identified. EHMT2/G9a and EHMT1/GLP are key SET domain containing
lysine methyltransferases that catalyze di-methylation of histone H3 lysine 9 (H3K9me2) resulting in
transcriptional repression of target genes (22). De-regulated expression and function of G9a, and to a
lesser extent GLP, have been reported in cancers of different origins (23-34). In addition to its
canonical role in gene repression, G9a can also activate genes through association with co-activator
complexes in a context dependent manner (23). However, the signaling pathways that G9a regulates
and its downstream effectors are poorly understood. In the context of skeletal myogenesis we and
others have shown that G9a inhibits muscle differentiation and promotes proliferation of myoblasts
(35-38). We therefore hypothesized that G9a expression may be de-regulated in ARMS and
contribute to tumorigenesis.
Here we provide evidence that the G9a pathway is central to ARMS tumorigenesis. Using an
epigenetic screen, we demonstrate that G9a inhibitors potently impact viability of ARMS cell lines.
Down regulation of G9a expression or pharmacological inhibition of its activity not only reduces tumor
cell proliferation and invasion, but also results in myogenic differentiation. In vivo, reduction of G9a
function reduces tumor growth in xenograft mouse models. Transcriptomic and ChIP-sequencing
(ChIP-Seq) analysis demonstrate that G9a directly inhibits PTEN expression in a methylation-
dependent manner, resulting in the upregulation of phospho-AKT levels and RAC1 activity. Re-
expression of constitutively active RAC1 reverses the impact of G9a deficiency in tumor growth. Our
data suggest that the PTEN-RAC1 axis downstream of G9a is central to ARMS tumorigenesis.
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Materials and methods
Cell culture and stable cell lines
Primary human skeletal muscle myoblasts (HSMMs) (Lonza Inc., Basel, Switzerland) were cultured in
growth medium (GM) (SkGM-2 BulletKit). ARMS cell lines RH30, RH4 and RH41 were provided by
Peter Houghton (Nationwide Children’s Hospital, Ohio, USA) and Rosella Rota (Bambino Gesu
Children’s Hospital, Rome, Italy) and cultured in RPMI 1640 with L-Glutamine (Thermo Fisher
Scientific, Waltham, MA, USA) with 10% FBS (Hyclone, Logan UT, USA). ARMS cells lines were
authenticated for PAX3-FOXO1, MyoD and myogenin expression by western blot as described (21).
Cell lines were tested at least every 6 months for myocplasma contamination using the Mycokit
detection kit from Biowest (K1000). After thawing, cell lines were not passaged more than 10 times.
The cell lines were maintained for no more than 3 passages between experiments. Where indicated,
cells were treated with 2.5µM UNC0642 (Sigma-Aldrich, St. Louis, MO, USA), or RAC1 inhibitor
NSC23766 (Tocris Bioscience, Bristol, UK) at 50µM (RH30) and 30µM (RH41) for 48hr. Control cells
were treated with equivalent concentrations of DMSO (Sigma-Aldrich). For transient knockdown
experiments, cells were transfected with 100nM of human G9a-specific siRNA (ON-TARGETplus
EHMT2 siRNA SMARTpool, Dharmacon, Lafayette, CO, USA) containing a pool of three to five 19–25
nucleotide siRNAs. Control cells were transfected with 100nM scrambled siRNA (ON-TARGETplus,
non-targeting pool, Dharmacon) using Lipofectamine RNAiMax (Thermo Fisher scientific). Cells were
analysed 48hr post-transfection for all assays. For generating stable knockdown cell lines, 293FT
cells were transfected with packaging plasmids plP1 (5µg) and plP2 (5µg), envelope plasmid
plP/VSV-G (5µg) (ViraPower™ Lentiviral Packaging Mix, Thermo Fisher Scientific) and 5µg lentiviral
expression constructs shRNA (pLKO.1, Mission shRNA DNA clone, Sigma-Aldrich) or shG9a (pLKO.1)
(#SHCLND-NM_025256 MISSION shRNA Plasmid DNA). 16hr post-transfection the cell supernatant
was replaced with basal DMEM. The cell supernatants were collected 48hr and 72hr post-transfection,
centrifuged and the viral pellet was resuspended in 200µl of RPMI medium as stock solution. RH30
cells at 40-50% confluency were transduced with shRNA control virus (control), or shG9a virus and
2µl polybrene (8mg/ml) (Sigma-Aldrich) in RPMI 1640 basal medium. 6hr post-transduction, cell
supernatants were replaced with RPMI medium (10% FBS) for 24hr. Transduced cells were selected
with 1µg/ml puromycin (Sigma-Aldrich) for three days. For rescue experiments, shcontrol and shG9a
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cells were transfected with 2μg of Rac1-GFP plasmid (pcDNA3-EGFP-Rac1-Q61L, Addgene,
Cambridge, MA, USA) using Lipofectamine 2000 with PLUS reagent (Thermo Fisher Scientific).
Drug sensitivity of ARMS cells to inhibitors of methyltransferases
RH30 and RH4 cells were treated for 8 days with the indicated compounds in 384 wells at three
different concentrations (3-1-0.3 µM). Viability at day 4 and 8 was scored by MTS assay and reported
as the ratio over control treated cells (with equivalent dilution of DMSO) (RED>control;
WHITE=control BLUE< control). The experiment was conducted in triplicate and (+)-JQ1 was used a
positive control.
Western blot analysis
Whole cell extracts were isolated using RIPA or SDS lysis buffer supplemented with protease
inhibitors (Complete Mini, Sigma-Aldrich). The following primary antibodies were used: anti-G9a
(#3306S, 1:300, Cell Signaling, Danvers, MA, USA), anti-PTEN (138G6) (#9559, 1:1000, Cell
signaling), anti-phospho-AKT (Ser473) (D9E) (#4060, 1:500, Cell signaling), anti-AKT (#9272, 1:1000,
Cell signaling), anti-H3K9me2 (#9753S, 1:1000, Cell Signaling), anti-RAC1 (23A8) (#05-389, 1:500,
Merck Millipore, Bedford, MA, USA), anti-cyclin D1 (H295) (#sc753, 1:250, Santa Cruz Biotechnology,
Dallas, Texas, USA), anti-ß-actin (#A2228, 1:10,000; Sigma-Aldrich), anti-H3 (#ab1791, 1:10,000;
Abcam, Cambridge, MA, USA), anti-GAPDH (14C10) (#2118, 1:500, Santa Cruz Biotechnology).
Appropriate secondary antibodies (IgG-Fc Specific-Peroxidase) of mouse or rabbit origin (Sigma-
Aldrich) were used.
RAC1 activity assay
RAC1 activity in control and shG9a cells; and RH30 cells untreated and treated with UNC0642 was
detected using the active RAC1 detection kit (Cell Signaling). GST-PAK1-PBD fusion protein was
used to bind the activated form of GTP-bound RAC1. The complex was immunoprecipitated with
glutathione resin through GST-linked binding protein. Unbound proteins were washed off during
centrifugation and glutathione resin-bound GTPase was eluted with SDS buffer. Active RAC1 levels
were determined by western blot using anti-RAC1 antibody (Cell Signaling).
Proliferation, migration, and invasion assays
Cell proliferation was assessed by 5-bromo-2’-deoxy-uridine (BrdU) labelling (Roche, Basel,
Switzerland). Briefly, cells grown on coverslips were pulsed with 10 μM BrdU for 30min, fixed and
stained with anti-BrdU antibody (1:100) for 30min. The coverslips were washed, incubated with anti-
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mouse Ig-fluorescein antibody (1:200) and mounted onto a glass slide using DAPI (Vectashield,
Vector Laboratories, CA, USA). Images were captured using fluorescence microscope BX53
(Olympus Corporation, Shinjuku, Tokyo, Japan).
For transwell migration assays, cells were seeded in a six-well dish at a confluency of 1x105 cells/well.
Where appropriate, cells were treated with UNC0642 (2.5µM) or siG9a (100nM) for 48hr, and serum
starved for 24hr. 5x104 cells were seeded into ECM (Matrigel, collagen I, Corning, NY, USA) coated
polycarbonate membrane inserts (8.0μm pore size) and placed in a 24-well plate containing RPMI
1640 (10% FBS). After 24 hr, cells that migrated to the bottom surface of the insert were fixed with
4% paraformaldehyde and stained with 6% crystal violet for 20 min. Cells were then counted based
on five field digital images taken at 10X magnification using EVOS XL core imaging system
AMEX1000 (Thermo Fisher Scientific).
For wound healing assays, two wounds were created perpendicular to each other using a 200l
yellow tip in the plates with cells at approximately 95% confluency. The wound was then monitored at
24hr. The migratory capacity was calculated as percentage of wound closure with respect to zero-
time point. The cells were fixed as imaged as described above.
Immunofluorescence assays
For differentiation assays, RH30 and RH41 cells were cultured for 3-5 days in RPMI 1640
supplemented with 2% horse serum (Gibco, Carlsbad, CA, USA) at 90-95% confluency. Cells were
fixed with 4% paraformaldehyde, and incubated with anti-Myosin Heavy Chain (MHC; R&D Systems,
Minneapolis, MN, USA) (1:500, 1hr at RT) followed by secondary goat anti-Mouse IgG (H+L) Highly
Cross-Adsorbed Secondary Antibody, Alexa Fluor 568 (Thermo Fisher scientific). Coverslips were
mounted with DAPI (Vectashield, Vector Laboratories, CA, USA) and imaged using upright
fluorescence microscope BX53 (Olympus Corporation).
Transcriptome analysis and Quantitative real-time polymerase chain reaction (Q-PCR)
Multiplex gene expression analysis was carried out using PanCancer pathway Panel (#XT-CSO-
PATH1-12, Nanostring) using RNA from two biological replicates of RH41 cells transfected with
scrambled siRNA or siG9a. RNA clean-up was performed using RNeasy MiniElute Clean up kit
(Qiagen). The hybridized RNA was scanned using nCounter digital analyser. The RCC files
generated from nCounter were analysed using nSolver 3.0 software. Raw counts were normalized to
the geometric mean of positive control and housekeeping genes. To identify genes and pathways that
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are deregulated, PanCancer Pathway Advanced Analysis module was applied. The PanCancer
pathway data are compliant with MIAME guidelines and have been deposited in NCBI Gene
Expression Omnibus (GEO) Database [GEO accession number GSE118777]. For Q-PCR, total RNA
was extracted using Trizol (Thermo Fisher Scientific) and quantified using Nanodrop. Messenger RNA
(mRNA) was converted to a single-stranded complementary DNA (cDNA) using iScript cDNA
Synthesis Kit (Bio-Rad). Q-PCR was performed using Lightcycler 480 SYBR Green 1 Master Kit
(Roche). PCR amplification was performed as follows: 950 C 5 minutes, followed by 95
0 C for 10s,
annealing at 600 C for 10s, followed by 45 cycles at 72
0 C for 10s. Melting curves were generated and
tested for a single product after amplification. Light Cycler 480 software (version 1.3.0.0705) was
used for analysis. CT values of samples were normalized to internal control GAPDH to obtain delta
CT (ΔCT). Relative expression was calculated by 2−ΔCT
equation. Q-PCR was done using reaction
triplicates and at least two independent biological replicates were done for each analysis.
Representative data are shown. Error bars indicate the mean ± standard deviation. Primer
sequences for PTEN, RAC1 and PI3KR1 have been described (39-41). Primers for G9a are: 5'-
TGGGCCATGCCACAAAGTC-3' and 5'-CAGATGGAGGTGATTTTCCCG-3'. The transcriptome data
are compliant with MIAME guidelines and have been submitted to the GEO repository [accession
number GSE118777].
Chromatin immuno-precipitation (ChIP)
Chromatin immunoprecipitation-sequencing (ChIP-Seq) was performed with anti-G9a antibody
(Abcam, Cambridge, MA, USA). 20 million RH41 cells were treated with protein crosslinker DSG (Di-
Succinimidyl Glutarate) followed by chromatin crosslinking with 1% formaldehyde. Cells were lysed
with Farnham lysis buffer (5mM PIPES pH 8.0, 85mM KCl, and 0.5% NP-40, with protease inhibitors)
and passed through 27 gauge needle. Crude nuclear preparation was collected by centrifugation and
lysed with RIPA buffer (1X PBS, 1% NP-40, 0.5% sodium deoxycholate, 0.1% SDS with protease
inhibitor). Chromatin shearing was performed using Bioruptor (Diagenode). Washes were performed
using buffers provided in the ChIP kit (#17-295, Mercmillipore, Bedford, MA, USA). Chromatin
complex was eluted with elution buffer, reverse crosslinked and treated with proteinase K. DNA was
extracted using phenol-chloroform-isoamyl alcohol. 30ng ChIPed DNA and corresponding input were
used for ChIP-seq library construction (New England Biolabs). Library fragment size was determined
using the DNA 1000 Kit on the Agilent Bioanalyzer (Agilent Technologies). Libraries were quantified
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by qPCR using the KAPA Library Quantification Kit (KAPA Biosystems). Libraries were pooled in
equimolar and sequencing (150bp pair-end) was performed on the lllumina MiSeq at the Duke-NUS
Genome Biology Facility, according to manufacturer’s protocol (Illumina). The total analysable reads
were computed after filtering reads with low mapping score (MAPQ<10) and PCR duplicated reads.
To evaluate immunoprecipitation, the fraction of reads in peaks (FRiP) was computed using deeptools.
The library of G9a ChIP-seq had a FRiP score of 23%, indicating that the library achieved successful
global ChIP enrichment. Prediction of G9a binding sites was done using MACS2 to capture both
broad (q-value < 0.10) and narrow peaks (q-value < 0.05). The prediction revealed 21,506 binding
sites. The unique number of predicted binding sites from chr1-chrX was used in the analysis. The
average fold enrichment of multiple peaks from the same binding site were computed. To assess
whether the distribution of G9a bindings at promoters, gene body and intergenic regions is not
obtained from random chance, we compared the total number of G9a bindings overlapping with each
genomic feature against a random distribution. Particularly, we shuffled the location of G9a binding
sites and computed the total number of random bindings overlapping with each genomic feature. This
procedure was repeated for 10,000 iterations. Empirical p-values were then computed as the fraction
of total iterations where the number of random bindings is greater than the observed number of G9a
bindings overlapping with each feature. We detected 21,506 G9a binding sites across the genome, of
which 44% (n=9,560) localized at promoters, 31% (n=6,623) at gene bodies and 25% (n=5,323) at
inter-genic regions. Interestingly, the overlap of G9a binding sites at promoters was ~3x higher than
the random bindings at promoters, and was statistically significant (empirical p-value < 0.0001),
highlighting that the distribution of G9a binding at promoter was not obtained by random chance. On
the other hand, the median occurrence of random binding events at gene bodies and inter-genic
regions were 9,020 and 9,425 respectively, indicating that the observed G9a binding sites at these
genomic features were under-represented compared to random events. Top 5,000 predicted G9a
binding sites showing high fold enrichment were used for the association analysis with biological
processes. This was performed using the GREAT tool (http://great.stanford.edu/public/html/) with
default parameters. To ensure consistency of the results, different top predicted binding sites
(n=2,500; n=10,000) were also tested. Sequencing reads were mapped against hg19 and G9a bound
regions were identified by MACS2000 predicted binding sites showing high fold enrichment (binding
signal to background ratio). The observation remained consistent even by using a different number of
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predicted sites (n=2,500; n=10,000), suggesting that the associations with biological processes are
robust and independent from the arbitrary number of sites. The ChIP-Seq data are compliant with
MIAME guidelines and have been deposited in the NCBI GEO database [accession number
GSE118666]. ChIP-PCR was done as previously described (38). Briefly, 5 × 106 cells were cross-
linked with 1% formaldehyde for 10 min at 37°C, sonicated and ChIP was carried out according to the
kit protocol (Merck Millipore). Immunoprecipitates were reverse-cross-linked, DNA extracted, and Q-
PCR performed as described above. 10% of the initial DNA was used as input control. Relative
enrichment was calculated using 2−ΔCT
equation. The following antibodies were used for ChIP assays:
ChIP-grade anti-G9a (Abcam), anti-H3K9me2 (Abcam), anti-H3K9ac (Abcam), anti-H3K27ac
(Abcam). Primers sequences for CSF2 and PGC1a have been described (42). G9a occupancy at the
PTEN was analysed using the primers: Forward: 5′-GCAGGAAGGGTTGGGGTTCC-3′ Reverse: 5′-
GGATACACGGGCCACAGTCG-3’ as described (42).
Mouse xenograft experiments and immunohistochemistry (IHC)
All animal procedures were approved by the Institutional Animal Care and Use Committee. 6 X 106
RH30 cells were injected subcutaneously into the right flank of 6-week-old CrTac:NCr-Foxn1 nude
female mice (InVivos, Singapore) as described (21). When tumor nodules were palpable, mice
(n=10/group) were injected intraperitoneally with UNC0642 at 5mg/kg body weight every alternate
day. Control group was injected with DMSO. Tumour diameter was measured using digital vernier
callipers on alternate days. Tumour volume was calculated using the following formula: V = (L ×W
×W)/2, where V is the tumour volume, W is the tumour width, and L is the tumour length. Body weight
was measured every alternate day. Once the tumors reached 1.5cm in diameter in the control cohort,
mice were euthanized and resected tumors were fixed in paraformaldehyde. Paraffin sections were
stained with haematoxylin and eosin (H&E) or analysed by IHC. In an alternative approach, shcontrol
and shG9a cells were injected in mice and tumor growth was monitored as described above. For IHC,
slides were deparaffinised and rehydrated followed by antigen retrieval with Na-citrate buffer (pH 7.0).
Sections were incubated overnight at 4°C with anti-G9a (1:200, Abcam), anti-H3K9me2 (Abcam),
anti-Ki67 (1:100, Santa Cruz Biotechnology), anti-active caspase 3 (Asp-175, 1:100; Cell Signaling),
anti-RAC1 (23A8) (Merck Millipore), anti-active RAC1 (1:100, New East Biosciences, King of Prussia,
PA, USA) antibodies followed by biotinylated goat anti-rabbit/anti-mouse IgG (H+L) secondary
antibody (Vector Laboratories) for 1hr at 37°C. Sections were washed and incubated with Vectastain
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Avidin–Biotin Complex (Vector Laboratories) for 20 min at 37°C. After incubation, DAB substrate
(Vector Laboratories) was added until colour development. Sections were counterstained with
haematoxylin (Sigma-Aldrich). Slides were dehydrated and mounted using DPX (Sigma-Aldrich) and
imaged using BX53 Olympus microscope.
IHC on 15 primary fusion positive archival tumor tissues obtained from the National University
Hospital (NUH) and KK Women’s and Children Hospital in Singapore was done essentially as
described (21). Sections were stained with anti-G9a antibody (1:50 dilution, Cell Signaling). Three
normal muscle tissue sections were used as controls. Negative controls were performed using
secondary antibody only. Images were captured with Olympus BX43 microscope (Ina-shi, Nagano,
Japan). Approval for the study was obtained from the ethics committee (IRB) at NUS.
Statistical analysis
Error bars indicate mean ± standard deviation (SD) unless specified otherwise. Significance was
determined using Student’s t-test (two-sided) and p values less than 0.05 were considered statistically
significant (*p<0.05; **p<0.01; ***p<0.001).
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Results
G9a is an overexpressed and druggable target in ARMS
We performed a small molecule screen in order to identify druggable methyltransferases involved in
the tumorigenesis of ARMS. Three concentrations (3-1-0.3 µM) of 13 methyltransferases inhibitors
(provided by the Structural Genomic Consortium) were tested in two different cell lines, RH30 and
RH4, at two different time points (day 4-8). Viability was evaluated via an MTS assay (Fig. 1A). Blue
and red in the heatmap indicate a lower or higher number of cells, respectively, compared to the
control. In addition to JQ1 that inhibits Brd4 activity and has recently been shown to impact PAX3-
FOXO1 in RMS (19), the screen identified four drugs that show a strong effect on viability in both cell
lines: MS023 (PRMT type I inhibitor), GSK591 (PRMT5 inhibitor), UNC0638 and UNC0642 (G9a
inhibitors). We decided in this study to focus on the oncogenic activity of G9a. The effect of
modulating arginine methylation in ARMS will be described elsewhere. Consistent with the screen,
treatment of patient derived RH30 and RH41 cells with UNC0642 (Supplementary Fig. S1A and S1B),
or down regulation of G9a expression using small hairpin RNA (shRNA) (Supplementary Fig. S1C)
led to a striking reduction in colony formation (Supplementary Fig. S1D). These findings led us to
examine G9a expression in ARMS cell lines and tumor specimens. G9a mRNA and protein levels
were upregulated in RH30 and RH41 cell lines (Fig. 1B and C) relative to primary human skeletal
muscle myoblasts (HSMM). In addition, immunohistochemical analysis of sixteen archival PAX3-
FOXO1 fusion-positive ARMS tumour sections showed that G9a was overexpressed in all specimens
in comparison to normal human muscle (Fig. 1D).
Knockdown of G9a expression or activity suppresses proliferation and migration, and
enhances myogenic differentiation
To examine its function in ARMS, we used two approaches. We down regulated endogenous G9a
expression either stably (Supplementary Fig. S1C), or transiently, using small interfering RNA (siRNA)
in RH30 (Supplementary Fig. S2A) cell line. In addition, we inhibited its catalytic activity using
UNC0642 (UNC) for 48hr which resulted in reduced H3K9me2, a signature of G9a activity
(Supplementary Fig. S1A and S1B). A striking reduction in cellular proliferation in shG9a cells was
apparent by BrdU assays (Fig. 2A) that was confirmed by growth curve assays (Fig. 2B). A similar
impact on cellular proliferation was seen in RH30 cells upon transient G9a knockdown (siG9a) and
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UNC treatment compared to their respective controls (Supplementary Fig. S2B). Interestingly, an
increased propensity to undergo myogenic differentiation was also apparent by immunostaining with
myosin heavy chain (MHC) antibody in shG9a cells (Fig. 2C). These results were validated in siG9a
RH30, as well as in response to UNC treatment (Supplementary Fig. S2C). Consistently, expression
of myogenic markers was elevated in siG9a and UNC treated RH30 cells (Supplementary Fig. S2D).
Since G9a has been reported to influence migratory and invasive capacity of cancer cells (22-24), we
tested its involvement in migration by wound healing assays. Wound closure was monitored for 24hr
in G9a knockdown cells or cells that had been pre-treated with UNC0642 for 48 hr. Defective
migration was observed in shG9a cells (Fig. 2D), RH30 siG9a cells (Supplementary Fig. S2E) as well
as upon treatment with UNC0642 compared to respective control cells. In addition, significantly fewer
G9a knockdown cells and UNC0642 treated cells invaded through matrigel relative to controls (Fig.
2E and Supplementary Fig. S2F). We further validated the effects of G9a depletion in a second
PAX3-FOXO1+ ARMS cell line RH41. Both siG9a (Supplementary Fig. 3A) and UNC treated cells
showed reduced proliferation relative to their controls (Supplementary Fig. 3B). Myogenic
differentiation was increased as seen by MHC staining and elevated Myh1 expression
(Supplementary Fig. 3C-D) in siG9a and UNC treated cells whereas cellular migration and invasion
were decreased (Supplementary Fig. 3E-F).
PI3K signaling pathway is differentially regulated in ARMS cells silenced for G9a
To identify the molecular pathways and functional targets downstream of G9a, we performed
Nanostring PanCancer pathway analysis using RNA from control and siG9a cells. Global significance
scores indicated that PI3K signaling was among the most differentially regulated pathways (Fig. 3A
and Supplementary Table S1A). Of the 55 genes that were significantly altered by loss of G9a
expression, several genes in PI3K signaling pathway were found to be deregulated (Fig. 3B and
Supplementary Table S1B). To validate these findings, we focused on PTEN, an upstream regulator
of the PI3K/AKT pathway. Consistent with the transcriptome analysis, PTEN mRNA was significantly
upregulated in G9a knockdown cells, as well as upon UNC0642 treatment in both cell lines compared
to respective controls (Fig. 3C and D) indicating that it is regulated by G9a in a methylation-dependent
manner. The expression of additional differentially expressed genes was validated by Q-PCR in siG9a
cells (Supplementary Fig. S4).
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G9a directly regulates PTEN expression and RAC1 activity
Since genome-wide direct targets of G9a in rhabdomyosarcoma are unknown, we performed ChIP
followed by high-throughput sequencing with a G9a-specific antibody. G9a occupancy was enriched
at promoters, gene bodies and intergenic regions (Fig. 3E) and 44% of the peaks were mapped to
promoters at the transcription start site (TSS) (Fig. 3F). Gene ontology analysis using GREAT showed
that genes with G9a peaks were significantly enriched for biological processes that include skeletal
muscle development, regulation of stem cell or mesenchymal cell proliferation (Fig. 3G). G9a
enrichment was evident at the PTEN promoter region by ChIP-Seq (Fig. 3H) and its occupancy was
validated by ChIP-PCR in RH30 and RH41 cells (Fig. 3I and Supplementary Fig. S5A). We then
analysed activation (H3K9ac) and repression (H3K9me2) marks at the PTEN promoter by ChIP-PCR
in control and shG9a cells, as well as upon UNC0642 treatment (Fig. 3J-K). Consistent with an
upregulation of its expression, H3K9ac was increased, whereas H3K9me2, a signature of G9a activity
was expectedly reduced. A similar trend with both histone marks was seen at the PTEN promoter in
response to UNC0642 treatment. In line with previous reports (42), we identified G9a occupancy at
the PGC1 and CSF2 promoters by ChIP-Seq that was validated by ChIP-PCR (Supplementary Fig.
S5B and S5C).
Given the repression of its expression by G9a, we examined levels of the PI3K-AKT pathway that is
regulated by PTEN. Upon UNC0642 treatment, as well as in siG9a cells, reduced phospho-AKT
levels were apparent indicating G9a regulates the PTEN-pAKT axis in a methyltransferase activity-
dependent manner (Fig. 4A). In addition, a clear downregulation of active-RAC1 levels was seen
upon UNC0642 treatment as well as in shG9a cells (Fig. 4B and C).
Re-expression of active RAC1 restores proliferative and metastatic capacity of shG9a cells.
Since RAC1 activity was down regulated by loss of G9a, we tested whether it is an effector of G9a.
We first determined whether inhibition of RAC1 activity mimics G9a deficiency in ARMS cell lines. We
used NSC23766, a small molecule inhibitor of active RAC1. Cells treated with NSC23766 showed
marked reduction of BrdU+ cells (Supplementary Fig. S6A). In addition, migration and invasion were
significantly reduced in cells treated with NSC23766 compared to controls (Supplementary Fig. S6B
and C). We then examined if re-expression of constitutively active RAC1 restores proliferation and
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15
motility defects observed in shG9a cells. To this end, we transfected control and shG9a cells with
constitutively active RAC1 (RAC1Q61L) or empty vector (Fig. 4D). The proliferative capacity of shG9a
cells was significantly rescued upon expression of RAC1Q61L-GFP compared to empty vector control
(Fig. 4E). In addition, there was significant rescue of motility in shG9a cells as seen by wound healing
and invasion assays (Fig. 4F-G).
G9a knockdown inhibits tumorigenicity in vivo
To validate the role of G9a in tumorigenesis in vivo, we generated xenograft models by injecting
RH30 cells subcutaneously in nude mice. We used two approaches. In the first, mice were treated
with UNC0642 once tumors were palpable. The control group was treated with DMSO. Tumor
volumes and body weights were measured. In the UNC0642 treated cohort, tumor volume was
significantly lower compared to the matched DMSO controls (Fig. 5A-C) with no significant change in
body weight (Fig. 5D). Tumors sections from DMSO control and UNC0642 treated groups were
analysed histologically and by immunohistochemistry. A significant reduction in Ki67 staining was
seen indicating reduced tumor cell proliferation. In addition, H3K9me2 staining was expectedly
reduced in the UNC0642 treated mice compared to controls (Fig. 5E). No significant changes were
seen in the staining for G9a and cleaved caspase 3. Interestingly, active-RAC1 levels were strongly
down regulated correlating with the in vitro data. Moreover, a significant increase in PTEN expression
was observed in the tumors (Fig. 5F). In the second approach, we subcutaneously injected control
and shG9a cells in nude mice. Tumor volume was significantly lesser in mice injected with shG9a
cells compared to controls (Fig. 5G-I), with no overt change in body weight (Fig. 5J). Ki67 and active
RAC1 levels were strikingly reduced whereas PTEN expression was elevated in the tumors (Fig. 5L).
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16
Discussion
G9a has been reported to be overexpressed in a variety of cancers (24-34), yet the signaling
pathways that it controls, and its transcriptional targets remain to be identified in different tumor types.
In this study, we report an unprecedented role of G9a in epigenetic regulation of PI3K pathway in
alveolar rhabdomyosarcoma. Through transcriptome and ChIP-Seq analysis, we identified PTEN to
be a direct G9a target gene. We show G9a occupancy and its signature H3K9me2 repressive marks
at the PTEN promoter. Loss of G9a expression or pharmacological inhibition of its methyltransferase
activity relieved repression of PTEN expression resulting in reduced AKT and RAC1 activity and
consequently impaired proliferation and migration of tumor cells. Interestingly, inhibition of G9a also
resulted in an increased propsensity of tumor cells to undergo differentiation. Our results demonstrate
that G9a not only sustains oncogenic signaling in the tumors, but also blocks their ability to undergo
myogenic differentiation. Our data is consistent with a recent study that showed G9a-mediated
repression of PTEN expression in non small cell lung cancer (43).
Previous studies have shown that the PI3K/AKT signaling is elevated in ARMS. This increase stems
from both elevated growth factors such as IGF-II and FGFR4 which are targets of PAX3-FOXO1, as
well as decreased PTEN levels (16,17). In addition to functioning as an antagonist of PI3K/AKT
pathway, PTEN also dephosphorylates other substrates such as p125FAK
(44). However, increased
motility of PTEN-/- fibroblasts occurs due to increased AKT and RAC1 activity without changes in
p125FAK
levels indicating that RAC1 mediates the effects PTEN in cell migration (45). Our data is in
line with these findings. PTEN expression is de-repressed by UNC0642 which correlates with reduced
RAC1 activity. Several positive and negative regulators of PTEN expression have been described
(46). Among these, the zinc finger transcription factors Snail and Slug have been shown to repress
PTEN expression. Interestingly, G9a has been found to interact with Snail (25) to mediate EMT in
breast cancer. These studies together with our data suggest that Snail may recruit G9a to the PTEN
promoter. We note that RAC1 mRNA is reduced in G9a knockdown cells, suggesting that G9a may
impact RAC1 activity through additional mechanisms.
Similar to G9a, RAC1 is a key regulator of cell migration, impairs cell cycle exit, and inhibits myogenic
differentiation (35-38, 47, 48). In addition, active RAC1 levels are elevated in ARMS (48). We show
that a RAC1 inhibitor mimics the effects of a G9a inhibitor suggesting that G9a and RAC1
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17
downregulation have similar phenotypic outcomes. Since re-expression of constitutively active RAC1
rescues the effects of G9a deficiency, our data demonstrate that RAC1 is a downstream effector of
G9a rather than functioning in a parallel pathway to regulate tumorigenesis.
The PI3K/AKT signaling is hyperactivated in many cancers and contributes to survival and growth of
tumor cells. Therefore targeting this signaling axis provides an attractive opportunity for therapeutic
intervention (49,50). While a few tyrosine kinase inhibitors have now been approved by the Food and
Drug Administration (FDA), development of resistance remains a major challenge with PI3K inhibitors.
AKT inhibitors have also met with limited clinical success. Similarly, no clinically effective drugs
targeting RAC1 activity are available to date (51). Recent bioinformatics analyses of cancer genome
databases implicate several methyltransferases and acetyltransferases in various human cancers
including rhabdomyosarcoma (18, 52). In particular, mis-regulation of lysine methylation has been
reported in various cancers that makes proteins that govern this modification attractive drug targets.
Our studies suggest that the use of selective G9a inhibitors to increase PTEN expression and
dampen AKT/RAC1 activity could provide a novel therapeutic approach in ARMS.
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18
DATA AVAILABILITY
The ChIP-Seq data has been deposited in GEO under the accession number GSE118666.
The PanCancer pathway data been deposited in GEO under the accession number GSE118777.
ACKNOWLEDGEMENTS
We thank Peter Houghton and Rosella Rota for ARMS cell lines, and Ooi Wen Fong, Genome Institue
of Singapore for bioinformatics analysis. This work was supported by a National Medical Research
Council grant (NMRC/CBRG/0063/2014) to R.T. and E.G.
A.B is supported by the President’s Graduate Scholarship at the National University of Singapore.
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19
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Figure 1. G9a is overexpressed in fusion-positive ARMS. A, Drug sensitivity of ARMS cell lines RH30
and RH4 to methyltransferase inhibitors was tested. Cell lines were treated up to 8 days with the
indicated compounds in 384 wells (3-1-0.3 µM). Viability at day 4 and 8 was scored by MTS assay
and reported as the ratio over control treated cells (with equivalent dilution of DMSO) (RED>control;
WHITE=control BLUE< control). The experiment was conducted in triplicates and (+)-JQ1 was used a
positive control. The results showed that MS023, UNC0642, UNC638 and GSK591 had a strong
effect on the viability of ARMS cell lines. The IC50 of MS023, UNC0642, UNC0638 and GSK591 in
RH30 and RH4 after 8 days is shown. B-C, G9a mRNA (B) and protein (C) levels were examined in
primary human skeletal muscle myoblasts (HSMM) and ARMS cells lines (RH30 and RH41) by Q-
PCR and western blotting respectively. Error bars indicate the mean of Q-PCR triplicates in each
experiment ± SD. The results are representative of two independent experiments. Numbers indicate
the molecular weight of proteins. D, G9a expression in three normal skeletal muscles (Normal 1-3)
was compared to 15 archival ARMS patient tumor specimens (#1-15) by immunohistochemistry using
anti-G9a antibody. Higher nuclear G9a staining was apparent in all tumors albeit to varying levels.
The upper right inset shows a zoomed in image. Negative controls were done by immunostaining
three tumor specimens with secondary antibody alone. Scale bar: 50 μm.
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Figure 2.
Stable knockdown of G9a alters proliferation, differentiation and motility. A, BrdU assays were done
with control and shG9a cells. The percentage of proliferating cells was quantified. B, Growth curve
assay was performed by seeding 0.1 million control and shG9a RH30 cells. Cells were trypsinized
every 24hr and cell numbers were counted for a period of 5 days. C, Control and shG9a cells were
differentiated for 5 days in 2% horse serum containing medium. Cells were immunostained with anti-
MHC antibody (red). D-E, Motility of control and shG9a cells was analysed by wound healing assays
to assess migration (D) and by matrigel Boyden chamber assays (E). Migration of cells was monitored
over a 24hr period. Error bars indicate the mean ± SD.
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Figure 3. PTEN is a direct G9a target gene. A, Nanostring PanCancer pathway analysis was
performed with two biological replicates of control and siG9a cells. Gene Set Analysis revealed that
PI3K signaling was among the most significantly altered pathways with deregulated expression of a
large number of genes. B, A list of genes in the PI3K-AKT pathway that were significantly altered in
siG9a cells relative to controls. The significance levels are shown. C-D, PTEN mRNA levels in control
and siG9a cells, as well as upon UNC treatment was analyzed by Q-PCR in RH30 and RH41 cells.
The results are representative of two independent experiments. Error bars indicate the mean of Q-
PCR triplicates in each experiment ± SD. E, Genome-wide G9a occupancy was analyzed by ChIP-
Seq. A pie chart showing genomic distribution of G9a peaks at promoters, gene body and intergenic
regions. Highest G9a occupancy was observed at the promoter regions followed by gene body and
intergenic regions. F, Histogram showing G9a distribution at the TSS +/- 20kb. G, MsigDB analysis
using GREAT (Genomic Regions Enrichment of Annotations Tool) showed biological processes that
are significantly regulated by G9a. H, Genomic snap shot using UCSC genome browser showing G9a
enrichment at the PTEN promoter. I, ChIP-PCR validation showed G9a occupancy at the PTEN
promoter. The results are representative of two independent experiments. Error bars indicate the
mean of Q-PCR triplicates in each experiment ± SD. J-K, ChIP-PCR analysis in control and shG9a
cells for H3K9ac and H3K9me2 marks at the PTEN promoter. In addition, ChIP-PCR analysis was
done in DMSO control and UNC0642 treated cells for H3K9ac and H3K9me2 enrichment. Error bars
indicate the mean of Q-PCR triplicates in each experiment ± SD. The results are representative of two
independent experiments.
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27
Figure 4.
RAC1 is an effector of G9a-dependent oncogenic phenotypes. A, PTEN expression and p-AKT
(Ser473) levels were analysed by western blot upon treatment with 2.5µM UNC0642 for 48hr, as well
as in control and siG9a cells. The results are representative of two independent experiments. B,
RAC1 activity was analysed 48hr after UNC0642 treatment by immunoprecipitation with anti-active
RAC1 antibody. The blots were immunoblotted with total RAC1 antibody. The results are
representative of two independent experiments. C, RAC1 activity was examined in control and shG9a
cells. Total RAC1 antibody was used as the loading control. The results are representative of two
independent experiments. D-G, Control and shG9a cells were transfected with constitutively active
RAC1-GFP plasmid. Expression of G9a and RAC1 was analysed by western blot using anti-G9a and
anti-GFP antibodies (D). BrdU assay was done to measure proliferation (E), matrigel trans-well assay
was done for invasion (F), and wound healing assay for migration (G). Quantification of data in each
assay is shown in the respective bar graphs. The results are representative of at least two
independent experiments. Error bars indicate the mean ± SD.
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28
Figure 5.
G9a inhibition impairs tumor growth in vivo. A-B, Nude mice (n=10/group) were injected with RH30
cells. When tumors were palpable, mice were treated with either vehicle DMSO or UNC0642 (5mg/kg
body weight). Three representative control and UNC0642 treated mice (M1, M2, M3) and resected
tumors are shown in A and B respectively. C, Relative tumor volume in control and UNC0642 treated
mice was measured every two days after DMSO or UNC0642 treatment. Day 0 (D0) refers to the day
injections with DMSO or UNC0642 were started. D, Body weight of control and UNC0642 treated
mice was measured every two days after treatment. E, Tumor sections from two independent DMSO
and UNC0642 treated mice were stained with H&E. Sections from the tumors were immunostained
with anti-Ki67, anti-caspase 3 (Casp3), anti-G9a, anti-H3K9me2, and anti-active RAC1 and total
RAC1 antibodies. F, Tumors were analyzed for PTEN and total RAC1 levels by western blot. G-H,
Control and shG9a RH30 cells were injected in nude mice (n=5/group). Mice were sacrificed at D12
after injection of cells. Representative images of mice and resected tumors are shown in G and H
respectively. I-J, Relative tumor volume and body weight of control and shG9a mice was measured
every two days. K, Tumor sections from two independent DMSO and UNC0642 treated mice were
stained with H&E. Sections were immunostained with anti-Ki67, anti-G9a, anti-active RAC1 and anti-
RAC1 antibodies. L, Tumor lysates from control and shG9a mice were analyzed by western blot with
anti-PTEN, anti-RAC1 and anti- actin antibodies.
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A
C
Bhat et al. Fig.1
B
D
RH4
Alveolar
(Pax3-Foxo1)
RH30
Alveolar
(Pax3-Foxo1)
DRUGS TARGETS
GSK343 EZH2, EZH1
SGC707 PRMT3
R)-PFI-2
(hydrochloride) SETD7
DMSO
BAY-598 SMYD2
A-366 G9a, EHMT1
SGC0946 Dot1L
UNC1999 EZH2, EZH1
A-196 SUV420H1/2
MS049 (HCl salt) PRMT4/6
MS023 (HCl salt) PRMT type I
UNC0642 G9a, EHMT1
UNC0638 G9a, EHMT1
GSK591 PRMT5
(+)-JQ1 BRD4
Day 4
DRUGS TARGETS
BAY-598 SMYD2
DMSO
R)-PFI-2
(hydrochloride) SETD7
SGC0946 Dot1L
SGC707 PRMT3
GSK343 EZH2, EZH1
A-366 G9a, EHMT1
MS049 (HCl salt) PRMT4/6
A-196 SUV420H1/2
MS023 (HCl salt) PRMT type I
UNC0642 G9a, EHMT1
GSK591 PRMT5
UNC1999 EZH2, EZH1
UNC0638 G9a, EHMT1
(+)-JQ1 BRD4
Day 8
3 µM 1 µM 0.3 µM 3 µM 1 µM 0.3 µM
RH4
Alveolar
(Pax3-Foxo1)
RH30
Alveolar
(Pax3-Foxo1)
3 µM 1 µM 0.3 µM 3 µM 1 µM 0.3 µM
0
2
4
6
8
10
12
Re
lati
ve e
xp
ressio
n
***
***
G9a
β-actin
- 160
- 42
Normal
Muscle
Negative
Control ARMS patient tumors (PAX3-FOXO1 positive )
1
2
1
2
3 3
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
DRUGS RH30 RH4
MS023 300 nM 2500 nM
UNC0642 300 nM 2000 nM
UNC0638 2000 nM 2000 nM
GSK591 <300 nM 750 nM
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0
20
40
60
control shG9a
*
% B
rdU
po
sit
ivit
y
DAPI MHC Merge
co
ntr
ol
sh
G9a
RH30
D5
co
ntr
ol
sh
G9a
0hr 24hr
RH30
A
C
D E
co
ntr
ol
RH30
sh
G9a
24hr
Bhat et al. Fig.2
co
ntr
ol
sh
G9a
RH30
DAPI MHC Merge
0
0.2
0.4
0.6
0.8
1
D0 D1 D2 D3 D4 D5
sh control shG9a
RH30 ***
*
*
Ce
ll d
en
sit
y *
10
6
B
0
20
40
60
80
100*
% M
igra
tio
n
control shG9a 0
20
40
60
80
100***
% I
nva
sio
n
control shG9a
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0
0.01
0.02
0.03
0.04
H3K9ac
**
%In
pu
t
0
0.05
0.1
0.15
0.2
0.25
0.3
H3K9me2
***
%In
pu
t
0
0.1
0.2
0.3**
H3K9me2
%In
pu
t
0
0.01
0.02
0.03
0.04*
H3K9ac
%In
pu
t
Bhat et al, Fig.3
A B
C
E
RH41 (PTEN mRNA)
0
0.5
1
1.5
2
2.5
Re
lati
ve e
xp
ressio
n
*
0
1
2
3
4
5
Re
lati
ve e
xp
ressio
n
*
RH30 (PTEN mRNA) D
G
H I
Re
lati
ve e
xp
ressio
n
*
0
1
2
3
4
Re
lati
ve e
xp
ressio
n
*
0
1
2
3
F
J RH30 (PTEN promoter) K RH30 (PTEN promoter)
*
RH30 (PTEN promoter)
%In
pu
t
00.010.020.030.040.050.06
-10*log10 (p-value)
Nu
mb
er
of
dif
fere
nti
ally
ex
pre
ssed
ge
ne
s
0
2
4
6
8
0 6 12 18 24 30
GNG4MMP9AKT3
PIK3R4RELA
FASAKT2
PIK3R1PTENRAC1
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A C
G
0
15
30
45
60
75 ** ***
**
% B
rdU
+ c
ells
0
50
100
150
200
250
% I
nva
sio
n
** *
*
D E
B
0
50
100
150
200
250
*** ***
% M
igra
tio
n
DA
PI
Brd
U
control control
+RAC1 shG9a shG9a
+RAC1
RH30
control control + RAC1
0h
r 2
4h
r
shG9a shG9a + RAC1
RH30
Bhat et al, Fig. 4
F
control control+RAC1
shG9a shG9a+RAC1
RH30
24hr
β-actin
PTEN
G9a
p-AKT
(Ser473)
RH41
-160
- 54
- 42
- 60
DMSO UNC
RH41
G9a
Active
RAC1
Total
RAC1
GAPDH
-160
- 21
-21
- 36
control shG9a
GAPDH
Active
RAC1
Total
RAC1
RH30
- 21
- 21
- 36
G9a
GFP
GAPDH
Rac1-GFP
control shG9a _ _ + + _ _ + +
RH30
- 160
- 27
- 36
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A
UN
C
DM
SO
M1 M2 M3
B
co
ntr
ol
sh
G9a
M1 M2 M3
co
ntr
ol
sh
G9a
Bo
dy w
t. (
g)
0
10
20
30
40
50
D0 D2 D4 D6 D8 D10 D12
control shG9a
0
10
20
30
40
D0 D2 D4 D6 D8 D10
DMSO UNC0642 (5mg/kg)
Bo
dy w
t. (g
) C
D
E
F
G H
J K
L
Bhat et al, Fig. 5
UN
C
DM
SO
I
RAC1
PTEN
β-actin
DMSO UNC DMSO UNC
M1 M2
- 54
- 21
- 42
RAC1
PTEN
β-actin
G9a
M1 M2
- 54
- 21
- 42
- 160
Re
lati
ve T
um
or
vo
lum
e
0
1000
2000
3000
4000
5000
D0 D2 D4 D6 D8 D10
DMSO
UNC0642(5mg/kg)
*
*
* **
Re
lati
ve T
um
or
vo
lum
e
0
2000
4000
6000
8000
D0 D2 D4 D6 D8 D10 D12
control
shG9a
*
*
*
Ca
sp
3
DMSO
Tumor 1
UNC
Tumor 2 Tumor 1 Tumor 2
H&
E
Ki6
7
H3
K9
me
2
G9
a
Ac
tive
RA
C1
To
tal
RA
C1
50µM
H&
E
Ki6
7
To
tal
RA
C1
G
9a
A
cti
ve
RA
C1
shG9a
Tumor 1 Tumor 2
control
Tumor 1 Tumor 2
50µM
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Published OnlineFirst March 4, 2019.Cancer Res Akshay V Bhat, Monica Palanichamy Kala, Vinay Kumar Rao, et al. critical for tumor growth in alveolar rhabdomyosarcomaEpigenetic regulation of the PTEN-AKT-RAC1 axis by G9a is
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