epigenetic regulation of the pten-akt-rac1 axis by g9a is ......mar 02, 2019 · 1 epigenetic...

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

Research. on April 10, 2021. © 2019 American Association for Cancercancerres.aacrjournals.org Downloaded from

Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited. Author Manuscript Published OnlineFirst on March 4, 2019; DOI: 10.1158/0008-5472.CAN-18-2676

mailto:[email protected]

http://cancerres.aacrjournals.org/

2

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.




3

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




4

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.




5

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




6

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-




7

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




8

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



https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE118777


9

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



http://great.stanford.edu/public/html/


10

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




11

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).




12

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




13

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).




14

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




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).




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




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.




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.




19

REFERENCES

1. Keller C, Guttridge DC. Mechanisms of impaired differentiation in rhabdomyosarcoma. FEBS

J 2013;280:4323-4334.

2. Linardic CM. PAX3-FOXO1 fusion gene in rhabdomyosarcoma. Cancer Lett 2008;270: 10-18.

3. Olanich ME, Barr FG. A call to ARMS: targeting the PAX3-FOXO1 gene in alveolar

rhabdomyosarcoma. Expert Opin Ther Targets 2013;17:607-623.

4. Wachtel M, Schäfer BW. PAX3-FOXO1: Zooming in on an "undruggable" target. Semin.

Cancer Biol 2018;50:115-123.

5. Cao L, Yu Y , Bilke S, Walker RL, Mayeenuddin LH, Azorsa DO, et al. Genome-wide

identification of PAX3-FKHR binding sites in rhabdomyosarcoma reveals candidate target

genes important for development and cancer. Cancer Res 2010;70:6497-6508.

6. Davicioni E, Finckenstein FG, Shahbazian V, Buckley JD, Triche TJ, Anderson MJ.

Identification of a PAX-FKHR gene expression signature that defines molecular classes and

determines the prognosis of alveolar rhabdomyosarcomas. Cancer Res 2006;66: 6936-6946.

7. Mercado G E, Xia SJ, Zhang C, Ahn E H, Gustafson DM, Laé M et al. Identification of PAX3-

FKHR-regulated genes differentially expressed between alveolar and embryonal

rhabdomyosarcoma: focus on MYCN as a biologically relevant target. Genes Chromosomes

Cancer 2008;47:510–520.

8. Bernasconi M, Remppis A, Fredericks WJ, Rauscher FJ, Schäfer BW. Induction of apoptosis

in rhabdomyosarcoma cells through down-regulation of PAX proteins. Proc Natl Acad Sci

USA 1996;93:13164-13169.

9. Williamson D, Missiaglia E, de Reyniès A, Pierron G, Thuille B, Palenzuela G, Thway K,

Orbach D, Laé M, Fréneaux P, Pritchard-Jones K, Oberlin O, Shipley J, Delattre O.Fusion

gene-negative alveolar rhabdomyosarcoma is clinically and molecularly indistinguishable from

embryonal rhabdomyosarcoma.J Clin Oncol. 2010;28: 2151-2158.

10. Shern JF, Chen L, Chmielecki J, Wei JS, Patidar R, Rosenberg M, et al. Comprehensive

genomic analysis of rhabdomyosarcoma reveals a landscape of alterations affecting a

common genetic axis in fusion-positive and fusion-negative tumors. Cancer Discov

2014;4:216-231.



https://www.ncbi.nlm.nih.gov/pubmed/24436047




20

11. Seki M, Nishimura, Yoshida K, Shimamura T, Shiraishi Y, Sato Y, et al. Integrated genetic

and epigenetic analysis defines novel molecular subgroups in rhabdomyosarcoma. Nat

Commun 2015;6:7557.

12. Anderson J, Gordon A, Pritchard-Jones K, Shipley J. Genes, chromosomes, and

rhabdomyosarcoma. Genes Chromosomes Cancer 1999;26:275-285.

13. Hemmings BA, Restuccia DF. PI3K-PKB/Akt pathway. Cold Spring Harb Perspect Biol 2012;4:

a011189.

14. Han J, Luby-Phelps K, Das B, Shu X, Xia Y, Mosteller RD, et al. Role of substrates and

products of PI 3-kinase in regulating activation of Rac-related guanosine triphosphatases by

Vav. Science 1998;23:279:558-560.

15. Kazanietz MG, Caloca MJ. The Rac GTPase in Cancer: From Old Concepts to New

Paradigms. Cancer Res 2017;77:5445-5451.

16. Cao L, Yu Y, Darko I, Currier D, Mayeenuddin LH, Wan X, et al. Addiction to elevated insulin-

like growth factor I receptor and initial modulation of the AKT pathway define the

responsiveness of rhabdomyosarcoma to the targeting antibody. Cancer Res 2008;68: 8039-

8048.

17. Wan X, Helman LJ. Levels of PTEN protein modulate Akt phosphorylation on serine 473, but

not on threonine 308, in IGF-II-overexpressing rhabdomyosarcomas cells. Oncogene 2003;22:

8205-8211.

18. Shern JF, Yohe ME, Khan J. Pediatric Rhabdomyosarcoma. Crit Rev Oncog 2015;20:227-

243.

19. Gryder BE, Yohe ME, Chou HC, Zhang X, Marques J, Wachtel M, et al. PAX3-FOXO1

Establishes Myogenic Super Enhancers and Confers BET Bromodomain Vulnerability.

Cancer Discov 2017;7: 884-899.

20. Böhm M, Wachtel M, Marques JG, Streiff N, Laubscher D, Nanni P, et al. Helicase CHD4 is

an epigenetic coregulator of PAX3-FOXO1 in alveolar rhabdomyosarcoma. J Clin Invest

2016;126: 4237-4249.

21. Bharathy N, Suriyamurthy S, Rao VK, Ow JR, Lim HJ, Chakraborty P, et al. P/CAF mediates

PAX3-FOXO1-dependent oncogenesis in alveolar rhabdomyosarcoma. J Pathol 2016;240:

269-281.








21

22. Shinkai Y, Tachibana M. H3K9 methyltransferase G9a and the related molecule GLP. Genes

Dev 2011;25:781-8.

23. Shankar SR, Bahirvani AG, Rao VK, Bharathy N, Ow JR, Taneja R. G9a, a multipotent

regulator of gene expression. Epigenetics 2013;8:16-22.

24. Chen MW, Hua KT, Kao HJ, Chi CC, Wei LH, Johansson G, et al. H3K9 histone

methyltransferase G9a promotes lung cancer invasion and metastasis by silencing the cell

adhesion molecule Ep-CAM. Cancer Res 2010;70:7830-7840.

25. Dong C, Wu Y, Yao J, Wang Y, Yu Y, Rychahou PG, et al. G9a interacts with Snail and is

critical for Snail-mediated E-cadherin repression in human breast cancer. J Clin Invest

2012;122:1469-1486.

26. Lee SH, Kim J, Kim WH, Lee YM. Hypoxic silencing of tumor suppressor RUNX3 by histone

modification in gastric cancer cells. Oncogene 2009;28:184-194.

27. Lehnertz B, Pabst C, Su L, Miller M, Liu F, Yi L, et al. The methyltransferase G9a regulates

HoxA9-dependent transcription in AML. Genes Dev 2014;28: 317-327.

28. Son HJ, Kim JY, HahnY, Seo SB. Negative regulation of JAK2 by H3K9 methyltransferase

G9a in leukemia. Mol Cell Biol 2012;32:3681-3694.

29. Ding J, Li T, Wang X, Zhao E, Choi JH, Yang L, et al. The histone H3 methyltransferase G9A

epigenetically activates the serine-glycine synthesis pathway to sustain cancer cell survival

and proliferation. Cell Metab 2013;18:896-907.

30. Kang J, Shin SH, Yoon H, Huh J, Shin HW, Chun YS, et al. FIH Is an Oxygen Sensor in

Ovarian Cancer for G9a/GLP-Driven Epigenetic Regulation of Metastasis-Related Genes.

Cancer Res 2018;78:1184-1199.

31. Bao L, Chen Y, Lai HT, Wu SY, Wang JE, Hatanpaa KJ, et al. Methylation of hypoxia-

inducible factor (HIF)-1α by G9a/GLP inhibits HIF-1 transcriptional activity and cell migration.

Nucleic Acids Res 2018;6576-6591.

32. Wei L, Chiu D K, Tsang FH, Law CT, Cheng CL, Au SL, et al. Histone methyltransferase G9a

promotes liver cancer development by epigenetic silencing of tumor suppressor gene

RARRES3. J Hepatol 2017;67:758-769.




22

33. Wang YF, Zhang J, Su Y, Shen YY, Jiang DX, Hou YY, et al. G9a regulates breast cancer

growth by modulating iron homeostasis through the repression of ferroxidase hephaestin. Nat

Commun 2017;8:274.

34. Casciello F, Al-Ejeh F, Kelly G, Brennan DJ, Ngiow SF, Young A, et al. G9a drives hypoxia-

mediated gene repression for breast cancer cell survival and tumorigenesis. Proc Natl Acad

Sci USA 2017;114:7077-7082.

35. Wang J, Abate-Shen C. The MSX1 homeoprotein recruits G9a methyltransferase to

repressed target genes in myoblast cells. PLoS One 2012;7:e37647.

36. Ling BM, Bharathy N, Chung TK, Kok WK, Li S, Tan YH, et al. Lysine methyltransferase G9a

methylates the transcription factor MyoD and regulates skeletal muscle differentiation. Proc

Natl Acad Sci USA 2012;109:841-846.

37. Battisti V, Pontis J, Boyarchuk E, Fritsch L, Robin P, Ait-Si-Ali S, et al. Unexpected Distinct

Roles of the Related Histone H3 Lysine 9 Methyltransferases G9a and G9a-Like Protein in

Myoblasts. J Mol Biol 2016;428:2329-2343.

38. Rao VK, Ow JR, Shankar SR, Bharathy N, Manikandan J, Wang Y, et al. G9a promotes

proliferation and inhibits cell cycle exit during myogenic differentiation. Nucleic Acids Res

2016;44:8129-8143.

39. Clermont PL, Crea F, Chiang YT, Lin D, Zhang A, Wang JZ, et al. Identification of the

epigenetic reader CBX2 as a potential drug target in advanced prostate cancer. Clin

Epigenetics 2016;8:16.

40. Wang W, Chen Y, Wang S, Hu N, Cao Z, Wang W, et al. PIASxα ligase enhances SUMO1

modification of PTEN protein as a SUMO E3 ligase. J Biol Chem 2014;28:3217-30.

41. Cheng HW, Chen YF, Wong JM, Weng CW, Chen HY, Yu SL, et al. Cancer cells increase

endothelial cell tube formation and survival by activating the PI3K/Akt signaling pathway. J

Exp Clin Cancer Res 2017;36: 27

42. Purcell DJ, Khalid O, Ou CY, Little GH, Frenkel B, Baniwal SK, et al. Recruitment of

coregulator G9a by Runx2 for selective enhancement or suppression of transcription. J Cell

Biochem 2012;113:2406-14.








23

43. Wang L, Dong X, Ren Y, Luo J, Liu P, Su D, Yang X. Targeting EHMT2 reverses EGFR-TKI

resistance in NSCLC by epigenetically regulating the PTEN/AKT signaling pathway. Cell

Death Dis 2018;9:129.

44. Tamura M, Gu J, Matsumoto K, Aota S, Parsons R, Yamada KM. Inhibition of cell migration,

spreading, and focal adhesions by tumor suppressor PTEN. Science 1998;280:1614-1617.

45. Liliental J, Moon SY, Lesche R, Mamillapalli R, Li D, Zheng Y, et al. Genetic deletion of the

Pten tumor suppressor gene promotes cell motility by activation of Rac1 and Cdc42 GTPases.

Curr Biol 2000;10:401-404.

46. Haddadi N, Lin Y, Travis G, Simpson AM, Nassif NT, McGowan EM. PTEN/PTENP1:

'Regulating the regulator of RTK-dependent PI3K/Akt signaling', new targets for cancer

therapy. Mol Cancer 2018;17:37.

47. Heller H, Gredinger E, Bengal E. Rac1 inhibits myogenic differentiation by preventing the

complete withdrawal of myoblasts from the cell cycle. J Biol Chem 2001;276:37307-37316.

48. Meriane M, Charrasse S, Comunale F, Méry A, Fort P, Roux P, et al. Participation of small

GTPases Rac1 and Cdc42Hs in myoblast transformation. Oncogene 2002;21:2901-2907

49. Hennessy BT, Smith DL, Ram PT, Lu Y, Mills GB. Exploiting the PI3K/AKT pathway for

cancer drug discovery. Nat Rev Drug Discov 2005;4:988-1004.

50. Janku F, Yap TA, Meric-Bernstam F. Targeting the PI3K pathway in cancer: are we making

headway? Nat Rev Clin Oncol 2018;15:273-291.

51. Marei H, Malliri A. Rac1 in human diseases: The therapeutic potential of targeting Rac1

signaling regulatory mechanisms. Small GTPases 2017;8:139-163.

52. Helin K, Dhanak D. Chromatin proteins and modifications as drug targets. Nature 2013;502:

480-488.




24

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.




25

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.




26

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.




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.




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.




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




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




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




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




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




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

Updated version

10.1158/0008-5472.CAN-18-2676doi:

Access the most recent version of this article at:

Material

Supplementary

http://cancerres.aacrjournals.org/content/suppl/2019/03/02/0008-5472.CAN-18-2676.DC1

Access the most recent supplemental material at:

Manuscript

Authorbeen edited. Author manuscripts have been peer reviewed and accepted for publication but have not yet

E-mail alerts related to this article or journal.Sign up to receive free email-alerts

Subscriptions

Reprints and

[email protected] at

To order reprints of this article or to subscribe to the journal, contact the AACR Publications

Permissions

Rightslink site. Click on "Request Permissions" which will take you to the Copyright Clearance Center's (CCC)

.http://cancerres.aacrjournals.org/content/early/2019/03/02/0008-5472.CAN-18-2676To request permission to re-use all or part of this article, use this link



http://cancerres.aacrjournals.org/lookup/doi/10.1158/0008-5472.CAN-18-2676

http://cancerres.aacrjournals.org/content/suppl/2019/03/02/0008-5472.CAN-18-2676.DC1

http://cancerres.aacrjournals.org/cgi/alerts

mailto:[email protected]

http://cancerres.aacrjournals.org/content/early/2019/03/02/0008-5472.CAN-18-2676


epigenetic regulation of the pten-akt-rac1 axis by g9a is ......mar 02, 2019 · 1 epigenetic...

Documents