Biochem. J. (2012) 444, 323–331 (Printed in Great Britain)
323
doi:10.1042/BJ20112033
CARM1/PRMT4 is necessary for the glycogen gene expression programme
in skeletal muscle cells
Shu-Ching Mary WANG1 , Dennis H. DOWHAN1 , Natalie A. ERIKSSON and George E. O. MUSCAT2
Obesity Research Centre, Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland 4072, Australia
CARM1 (co-activator-associated arginine methyltransferase
1)/PRMT4 (protein arginine methyltransferase 4), functions as
a co-activator for transcription factors that are regulators of
muscle fibre type and oxidative metabolism, including PGC
(peroxisome-proliferator-activated receptor γ co-activator)-1α
and MEF2 (myocyte enhancer factor 2). We observed significantly
higher Prmt4 mRNA expression in comparison with Prmt1–
Prmt6 mRNA expression in mouse muscle (in vitro and in vivo).
Transfection of Prmt4 siRNA (small interfering RNA) into mouse
skeletal muscle C2C12 cells attenuated PRMT4 mRNA and
protein expression. We subsequently performed additional qPCR
(quantitative PCR) analysis (in the context of metabolism) to
examine the effect of Prmt4 siRNA expression on >200 critical
genes that control (and are involved in) lipid, glucose and energy
homoeostasis, and circadian rhythm. This analysis revealed a
strikingly specific metabolic expression footprint, and revealed
that PRMT4 is necessary for the expression of genes involved
in glycogen metabolism in skeletal muscle cells. Prmt4 siRNA
expression selectively suppressed the mRNAs encoding Gys1
(glycogen synthase 1), Pgam2 (muscle phosphoglycerate mutase
2) and Pygm (muscle glycogen phosphorylase). Significantly,
PGAM, PYGM and GYS1 deficiency in humans causes
glycogen storage diseases type X, type V/McArdle’s disease and
type 0 respectively. Attenuation of PRMT4 was also associated
with decreased expression of the mRNAs encoding AMPK
(AMP-activated protein kinase) α2/γ 3 (Prkaa2 and Prkag3)
and p38 MAPK (mitogen-activated protein kinase), previously
implicated in Wolff–Parkinson–White syndrome and Pompe
Disease (glycogen storage disease type II). Furthermore, stable
transfection of two PRMT4-site-specific (methyltransferase
deficient) mutants (CARM1/PRMT4 VLD and CARM1E267Q)
significantly repressed the expression of Gys1, Pgam2 and
AMPKγ 3. Finally, in concordance, we observed increased
and decreased glycogen levels in PRMT4 (native)- and VLD
(methylation deficient mutant)-transfected skeletal muscle cells
respectively. This demonstrated that PRMT4 expression and the
associated methyltransferase activity is necessary for the gene
expression programme involved in glycogen metabolism and
human glycogen storage diseases.
INTRODUCTION
(PRMT5, PRMT7 and PRMT9) have monomethylarginine and
symmetrical dimethylarginine enzymatic ability. PRMT-mediated
arginine residue methylation has been shown to affect the function
of target proteins by regulating their ability to interact with DNA,
RNA and other proteins. This ability to modulate protein function
has been found to have an impact on a range of cellular processes,
including proliferation, differentiation, DNA repair, transcription,
alternative splicing, signal transduction and cellular metabolism
[3–5].
The PRMTs tend to be universally expressed; however, they
do display some degree of tissue specificity, which can be
enhanced by alternative splicing. The physiological importance
of PRMTs has been demonstrated in mouse knockout studies;
PRMT4-null mice die perinatally [6] and PRMT1-null mice
die at embryonic day 6.5 [7]. This indicates that PRMTs not
only have non-overlapping roles, but they can also be vital
for existence. Initial studies found that PRMTs function as
co-activators for the NR (nuclear hormone receptor) superfamily,
and mediate nuclear/steroid receptor-dependent transcriptional
Protein arginine residue methylation was first detected over
40 years ago [1], but has only recently gained increased
interest and importance in the regulation of a wide variety
of signalling pathways and cellular functions. Protein arginine
residue methylation is a common post-translational modification
resulting in the addition of methyl groups from SAM (S-adenosylL-methionine) to guanidino nitrogen atoms on arginine residues
of target proteins. Therefore arginine residue methylation of
proteins results in a reduction of guanidino nitrogen atoms and
hydrogen bond potential that can alter molecular interactions
and influence biological functions [2]. In mammalian cells,
protein arginine residue methylation is catalysed by a family
of proteins called PRMTs (protein agrinine methyltransferases)
that can possess two distinct types of enzymatic abilities. Type I
enzymes (PRMT1, PRMT2, PRMT3, PRMT4, PRMT6 and
PRMT8) catalyse the formation of both monomethylarginine
and asymmetrical dimethylarginine, whereas the type II enzymes
Key words: co-activator-associated arginine methyltransferase
1 (CARM1), glycogen, glycogen synthase 1, protein arginine
methyltransferase 4, skeletal muscle cell.
Abbreviations used: AMPK/Ampk, AMP-activated protein kinase; BCA, bichinchoninic acid; CARM, co-activator-associated arginine methyltransferase;
COUP-TF, chicken ovalbumin upstream promoter-transcription factor; CT, expression level; DMEM, Dulbecco’s modified Eagle’s medium; ERR/Err,
oestrogen-related receptor; FDR, false detection rate; GAPDH, glyceraldehyde-3-phosphate dehydrogenase; GYS/Gys, glycogen synthase; IRS, insulin
receptor substrate; MAPK, mitogen-activated protein kinase; MEF, myocyte enhancer factor; MyHC, myosin heavy chain; NR, nuclear hormone receptor;
PEPCK, phosphoenolpyruvate carboxykinase; PGAM/Pgam, muscle phosphoglycerate mutase; PGC, peroxisome-proliferator-activated receptor γ coactivator; PI3K, phosphoinositide 3-kinase; PRMT/Prmt, protein arginine methyltransferase; PYGM/Pygm, muscle glycogen phosphorylase; qPCR,
quantitative PCR; qRT-PCR, quantitative real-time PCR; siRNA, small interfering RNA; SRC, steroid receptor co-activator; TCA, tricarboxylic acid; TLDA,
TaqMan Low Density Array.
1
These authors contributed equally to this study.
2
To whom correspondence should be addressed (email [email protected]).
c The Authors Journal compilation c 2012 Biochemical Society
324
S.-C.M. Wang and others
regulation [8,9]. NRs are hormone/ligand-dependent DNAbinding proteins that translate endocrine, metabolic and pathophysiological signals into gene regulation, and control metabolic
homoeostasis. NRs control lipid, carbohydrate and energy homoeostasis (and substrate utilization) in an organ/tissue-specific
manner, and are expressed in tissues with the largest mass, which
suggests a role in energy homoeostasis [10].
In the context of metabolism, PRMT4 has been found
to up-regulate the enzymes PEPCK (phosphoenolpyruvate
carboxykinase) and glucose 6-phosphatase expression in hepatocytes, indicating that PRMT4 has a significant role in regulating
gluconeogenesis [11]. Elevated gluconeogenesis can result in
peripheral insulin resistance and onset of Type II diabetes,
which implicates PRMT4 as a potential therapeutic target for
this disease. A study using HepG2 cells suggests that impaired
PRMT1 activity by PRMT1 siRNA (small interfering RNA)
attenuates the metabolic branch of insulin signalling involving
IRS (insulin receptor substrate)-2 and PI3K (phosphoinositide
3-kinase), reducing glucose uptake, leading to aberrant glucose
homoeostasis [12].
PRMTs are directly involved in the regulation of: (i) NR action,
(ii) insulin signalling, (iii) myogenic miRNA expression [13];
and (iv) PGC (peroxisome-proliferator-activated receptor γ coactivator)-1α and MEF2 (myocyte enhancer factor 2) activity,
which are important regulators of muscle fibre type, oxidative
metabolism, mitochondrial activity, endurance and exercise
response. We report that CARM1 (co-activator-associated
arginine methyltransferase 1; PRMT4) is the most abundant
PRMT expressed in skeletal muscle cells, and selectively
controls the pathways modulating glycogen metabolism, and
associated AMPK (AMP-activated protein kinase) and p38
MAPK (mitogen-activated protein kinase) expression. Prmt4
expression did not affect other aspects of glucose homoeostasis,
lipid metabolism and energy homoeostasis. Significantly, Prmt4
siRNA expression selectively targeted the mRNAs encoding Gys1
(glycogen synthase 1), Pgam2 (muscle phosphoglycerate mutase
2), Pygm (muscle glycogen phosphorylase), AMPK(α2/γ 3)
and p38 MAPK. Remarkably, aberrant expression of these
genes in humans is involved in the glycogen storage diseases
type X, type V/McArdle’s disease, type 0, Wolff–Parkinson–
White syndrome and Pompe Disease (glycogen storage disease
type II) respectively [14–19]. This suggested the Prmt4-dependent
gene expression programme regulates glycogen metabolism in
skeletal muscle cells.
sense 5 -CGGGCCUGGACUUGAUUUGGUTT-3 ; mPrmt4siRNA-B, sense 5 -UCACAGCCCUCUUUGCUAUGGTT-3 and
antisense 5 -UCACAGCCCUCUUGCUAUGGTT-3 . Silencer
Select siRNA (Ambion) was used as a negative control. Cells
were harvested for RNA or protein 48 h after transfection.
Generation of stable cell lines
For generation of C2C12 stable cell lines, cells were transfected
with 5 μg of vector control, pSG5, pSG5-Prmt4, pSG5-Prmt4
(VLD) or pSG5-Prmt4(E267Q) as described previously [20] in
75-cm2 flasks with 0.5 μg of pCMV-Neo at approximately 50 %
confluence using LipofectamineTM 2000 (Invitrogen) according
to manufacturer’s instructions. Cells were selected for neomycin
resistance expression 24 h after transfection by treatment with
600 mg/ml G418 (Astral Scientific). G418-resistant cells were
maintained in growth medium supplemented with 300 mg/ml
G418. For each cell line, a pool of polyclonal cells were passaged
into three replicates and differentiated into myotubes. RNA and
protein were extracted from each replicate independently.
RNA extraction and cDNA synthesis
Total RNA was extracted from C2C12 cells using TRI-Reagent
(Sigma) according to the manufacturer’s protocol. Following
treatment with Turbo DNaseI (Ambion) for 30 min at 37 ◦ C, RNA
was further purified with an RNeasy column (Qiagen). Superscript
III was used to synthesize cDNA from 2 μg of total RNA, with
random hexamers according to the manufacturer’s instructions
(Invitrogen). The cDNA was diluted to 300 μl with nuclease-free
water.
For mouse muscle samples, C57B1/6J mice were killed and
skeletal muscle was surgically excised. All samples were snapfrozen in liquid nitrogen. RNA and cDNA synthesis was carried
out as described in [20a]. All animal-related procedures were
approved by the Animal Experimentation Ethics Committees of
the University of Queensland and conformed to the Guidelines
for the Care and Use of Experimental Animals described by the
National Health and Medical Research Council of Australia.
qRT-PCR (quantitative real-time PCR)
qRT-PCR was performed as described previously [21,22].
TaqMan gene expression assays used were purchased from
Applied Biosystems. Statistical analysis was performed on the
three independent assays.
TaqMan low-density array
MATERIALS AND METHODS
Cell culture
Proliferating mouse C2C12 myoblasts (A.T.C.C.) were cultured
and maintained in DMEM (Dulbecco’s modified Eagle’s medium)
supplemented with 10 % heat-inactivated serum supreme
(Lonza). To induce myogenic differentiation of C2C12 cells
into post-mitotic multi-nucleated myotubes, cells were grown to
confluence and further cultured in DMEM supplemented with
2 % horse serum for 4 days.
cDNA synthesized from C2C12 cells transfected with siRNA,
control Silencer Select (si-SS) and si-Prmt4 were loaded on to
custom designed microfluidic TLDAs (TaqMan Low Density
Arrays; Applied Biosystems) to analyse the expression of
genes involved in metabolism (lipid, carbohydrate and energy
homoeostasis). Details of the methods and analysis used were
described previously [23,24]. Synthesized cDNA was also loaded
on to RT Profiler PCR Array (SABiosciences) consisting of 84
key genes involved in glucose and glycogen metabolism and
analysed with StatMiner software as described previously for
TLDAs [23,24].
Transfection of siRNA
To knock down Prmt4 expression, C2C12 cells were transfected
with an siRNA oligonucleotide duplex at a final concentration of
10 nM with RNAiMAX (Invitrogen) according to the manufacturer’s instructions. A mixture of two annealed siRNA duplexes
were transfected, and the sequences were: mPrmt4-siRNAA, sense 5 -ACCAAAUCAAGUCCAGGCCCGTT-3 and anti
c The Authors Journal compilation c 2012 Biochemical Society
Protein extraction and Western blotting
Total protein was extracted from C2C12 cells or myotubes
using lysis buffer (10 mM Tris/HCl, pH 8.0, 150 mM NaCl,
1 % Triton X-100 and 5 mM EDTA) containing protease and
phosphatase inhibitors (Roche). Lysates were passed through
a 27-gauge needle, and centrifuged (at 3000 g for 20 min at
PRMT4 regulates glycogenic genes
325
4 ◦ C) to remove the insoluble materials. The supernatants were
collected and total protein concentration was determined by BCA
(bichinchoninic acid) assay (Pierce) as per the manufacturer’s
instructions. Proteins extracted were resolved on SDS/PAGE
(10 % gels), and transferred on to PVDF membranes (Millipore).
The membranes were blocked for 1 h in Tris-buffered saline
(10 mM Tris/HCl, pH 7.5, and 150 mM NaCl) containing 0.01 %
Tween 20, and 5 % (w/v) non-fat dried skimmed milk powder
or 5 % (w/v) BSA for phospho-specific antibodies, followed
by an overnight incubation with either anti-PRMT4, antiPRMT6 (Bethyl Laboratories), anti-PRMT1, anti-PRMT5, antiAMPKα, anti-phospho-AMPKα (Cell Signaling Technology)
or anti-GAPDH (glyceraldehyde-3-phosphate dehydrogenase;
Santa Cruz Biotechnology) antibodies. The membranes were
incubated further with peroxidase-conjugated secondary antibody
for 1 h at room temperature (22 ◦ C). After washing with Trisbuffered saline, signals were detected with the ECL Plus Western
blotting detection system (GE Healthcare) and visualized by
autoradiography.
Determination of glycogen content
To measure the amount of glycogen, cells were scraped into
200 μl of water and homogenized. The homogenates were boiled
for 5 min to inactivate enzymes, centrifuged (at 17 000g for 5 min
at 4 ◦ C) and insoluble material was removed. Glycogen was
assayed using the Glycogen Assay Kit (BioVision) as per the
manufacturer’s instruction. An aliquot of the homogenates were
used for the measurement of protein concentration using the BCA
method. Results were normalized to the amount of protein. The
amount of glycogen is represented as the amount of converted
glucose per mg of total protein.
RESULTS
Prmt4 mRNA is the predominantly expressed PRMT in skeletal
muscle (in vitro and in vivo )
We isolated mRNA from: (i) post-mitotic multinucleated mouse
C2C12 skeletal muscle cells after 4 days of serum withdrawal;
and (ii) quadricep muscle from male C57Bl/6J mice (n = 4). We
utilized qRT-PCR and analysed relative Prmt1–Prmt6 mRNA
expression in differentiated skeletal muscle C2C12 cells, and
quadricep muscle from male C57Bl/6J mice. The analysis utilized
six internal controls, and expression was normalized against the
geNorm-selected most stable controls, with the least expression
variation. This qRT-PCR analysis demonstrates that Prmt4 is the
most abundantly expressed arginine residue methyltransferase in
skeletal muscle (in vitro and in vivo), and is significantly more
abundant than Prmt1–Prmt3, Prmt5 and Prmt6 (Figures 1A and
1B). We utilized quadricep muscle because it contains oxidative
(type I) and glycolytic (type II) muscle fibres. However, we also
explored Prmt1–Prmt6 mRNA expression in type I oxidative
soleus muscle and type II glycolytic gastrocnemius muscle. We
observed that Prmt1–Prmt6 mRNA expression in type I oxidative
soleus muscle mirrored the pattern found in skeletal muscle cells
and quadricep muscle (compare Figure 1C with Figures 1A and
1B). Interestingly, in type II gastrocnemius muscle, Prmt3–Prmt5
were abundantly expressed at significant levels (Figure 1D). In
summary, Prmt4 mRNA is an abundantly expressed arginine
residue methyltransferase in skeletal muscle (in vitro and in vivo).
In addition, we performed Western blotting analysis with antiPRMT1, anti-PRMT4, anti-PRMT5 and anti-PRMT6 antibodies
in proliferating myoblasts and differentiated myotubes (after 2
and 5 days of mitogen withdrawal). This clearly demonstrated that
PRMT1 and PRMT4 are expressed in skeletal muscle cells, with
Figure 1 Prmt4 mRNA is the predominately expressed PRMT both in vitro
and in vivo in skeletal muscle
Total RNA from (A) C2C12 muscle cells (n = 3), (B) mouse quadriceps muscle (n = 4), (C)
mouse soleus muscle (n = 4) and (D) mouse gastrocnemius muscle were analysed by qRT-PCR
for expression of Prmt1–Prmt6 . Results were normalized against median of geNorm selected
controls, B2m , Rplp0 and Pgk1 for muscle cells, and Ywhaz for quadriceps, soleus and
gastrocnemius muscles. Statistical significance was calculated using one-way ANOVA, Tukey’s
Multiple Comparison test; ***P < 0.001.
weak expression of PRMT5 and PRMT6 (Supplementary Figure
S1 at http://www.BiochemJ.org/bj/444/bj4440323add.htm).
Prmt4 siRNA expression attenuates PRMT4 mRNA and protein
expression in C2C12 cells
To explore the functional role of CARM1/PRMT4 (the most
abundantly expressed PRMT) in skeletal muscle cells, we utilized
the strategy of knocking down PRMT4 expression by transfection
of siRNAs, followed by functional profiling. The C2C12 cells
were transiently transfected with the siRNA oligonucleotide
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S.-C.M. Wang and others
Figure 2 Prmt4 siRNA expression significantly attenuates PRMT4 mRNA
and protein expression in C2C12 muscle cells
C2C12 cells were transfected with Prmt4 (si-Prmt4) and control (si-SS) siRNA for 48 h in
differentiation medium. (A) Total RNA was analysed by qRT-PCR for Prmt4 expression, and
normalized against 18S rRNA (n = 3). Statistical significance was calculated using Student’s
unpaired t test, ***P < 0.001. (B) PRMT4 protein levels were analysed by Western blotting
after siRNA treatment (n = 3). An aliquot of 20 μg of total protein from cells was resolved per
lane.
duplex in differentiation medium at a final concentration of
10 nM using RNAiMAX as described previously [25]. We
simultaneously transfected two siRNAs to efficiently target and
attenuate Mus musculus Prmt4 mRNA, mPrmt4-siRNA-A (nt
1986–2007, ACCAAATCAAGTCCAGGCCCG) and mPrmt4siRNA-B (nt 2057–2078, TCACAGCCCTCTTTGCTATGG).
qRT-PCR and Western blot analysis revealed a significant ∼3fold decrease in the mRNA encoding PRMT4, and an approximate
>4-fold decrease in PRMT4 protein expression in the si-Prmt4
(Figures 2A and 2B respectively).
Prmt4 siRNA expression selectively attenuates the expression of
the mRNAs involved in glycogen biosynthesis
We utilized two custom-designed Applied Biosystems
microfluidic TLDAs that encoded TaqMan primer sets for
genes implicated in the regulation of lipid, glucose and energy
homoeostasis, oxidative phosphorylation, circadian rhythm, etc.
(i.e. the majority of pathways regulating metabolism). The two
custom-designed TLDAs targeting >90 and >40 critical genes
that are involved in (and control) metabolism also incorporated
several normalization controls, including 18S rRNA, Gapdh,
GusB, Hprt and Arbp. These normalization controls span the
CT (expression level) range of the target genes. Differential
expression was identified by the application of the LIMMA
package for Bioconductor R embedded in the Integromics
StatMiner software suite. The geNorm software (within
the StatMiner V4.2 suite) was used to select the normalization
controls with the least expression variation. The empirical Bayes
statistic was used to assign significance, and Benjamini–Hochberg
FDR (false detection rate) correction was used to conservatively
filter data to adjust P values and control for false positives.
This very stringent analysis of significance (and genes
increased/decreased by >1.5-fold), demonstrated significant
(adjusted P values <0.01) and markedly selective suppression
of two genes involved in glycogen biosynthesis, Gys1, Pygm,
two AMPK isoforms (α2 and γ 3, Prkaa2 and Prkag3
respectively) and Mapk14 (p38α MAPK) when normalized
against the median of the StatMiner-geNorm selected most
stable controls, with the least expression variation (Arbp, Gapdh
c The Authors Journal compilation c 2012 Biochemical Society
Figure 3 Prmt4 siRNA expression selectively attenuates the expression of
genes involved in glycogen metabolism
C2C12 cells transfected with Prmt4 siRNA (si-Prmt4) or control-silencer select (si-SS) were
analysed with custom designed metabolic TLDA. (A and B) Histograms showing relative
quantification (log10 ), i.e. fold differences of genes after transfected with Prmt4 siRNA (relative
to control, si-SS). Metabolic genes were normalized to the median of the most stable geNorm
selected controls (Arbp , Gapdh and Gusb ) with the least expression variation. Results are
presented as valid when the CT values of the gene in the calibrator/reference and target samples
(i.e. si-SS and si-Prmt4 respectively) are less than 35 cycles. Fold differences are flagged as
target not detected or calibrator not detected when the CT value of the gene(s) in 50 % of the
target (si-Prmt4) or calibrator (si-SS) samples are 35 cycles or greater (than the threshold limit).
CT value of metabolic genes in both target and calibrator samples greater than the threshold limit
(35 cycles) are flagged as no detection, and were omitted from the histogram, including Adrb3 ,
Lep , Mc3r , Npy , Pck1, Retn , Slc2a2 and Ucp1. The Applied Biosystems SDS software was
utilized with the ABI7900 instrument to assign CT values beyond the arbitrarily set threshold
(CT 35) up to a maximum (CT 40), and used to calculate expression (in the calibrator and
target not detected genes). Significance is assigned after the application of the empirical Bayes
statistic, and Benjamini-Hochberg FDR analysis, adjusted P values, and the B scores (>0)
are considered for the final assignments (see the Materials and methods section). *P < 0.05;
**P < 0.01; ***P < 0.001.
and Gusb). Interestingly, aberrant AMPKα2/γ 3 (Prkaa2 and
Prkag3) expression (and AMPK polymorphism) and Mapk14
(p38α MAPK) have been associated with the regulation of
muscle glycogen levels [26,27] (Figures 3A and 3B). More
importantly, attenuation of Prmt4 selectively targeted/affected
genes involved in glycogen metabolism and not any other genes
involved in many other metabolic and signalling pathways.
Furthermore, profiling of the second custom-designed TLDA,
including additional metabolic modulators and regulators of
circadian rhythm, etc., did not display any significant differential
PRMT4 regulates glycogenic genes
327
Figure 4 Histogram showing relative quantification of significant metabolic
genes in Prmt4 siRNA-transfected C2C12 cells
Data are derived from Supplementary Table S2 (at http://www.BiochemJ.org/bj/444/
bj4440323add.htm), expressed as fold changes (log10 ) compared with control (si-SS),
normalized to the median of geNorm-selected controls (Gusb , Hprt1 and Hsp90ab1). Statistical
analysis was performed as detailed in Figure 3 and in the Materials and methods section.
*P < 0.05; **P < 0.01. si-Prmt4, Prmt4 siRNA.
expression with differences >1.5-fold (see Supplementary Table
S1 at http://www.BiochemJ.org/bj/444/bj4440323add.htm).
The selective glycogen footprint associated with the suppression of PRMT4 expression was investigated further by qRT-PCR
profiling of a real-time profiler PCR array (focused on glucose
metabolism) that profiled the expression of >80 genes involved
in multiple pathways, including glycolysis, gluconeogenesis,
TCA (tricarboxylic acid) and pentose phosphate cycles, and
glycogen metabolism (synthesis, degradation and regulation).
In concordance, we observed the significant differential
expression (and suppression, 1.6–2.3-fold) of only three genes,
Gys1, Pygm and Pgam2 (Figure 4 and Supplementary Table
S2 at http://www.BiochemJ.org/bj/444/bj4440323add.htm). This
analysis further underscored the selective targeting of gene
expression by PRMT4. In conclusion, this demonstrated that
PRMT4 expression was necessary for the expression of
genes involved in glycogen metabolism. Pgam2 is not a
glycogenic enzyme, it belongs to the glycolytic pathway,
where it catalyses the reversible reaction of 3-phosphoglycerate
to 2-phosphoglycerate. Pgam2 is the rate-limiting enzyme in
glycolysis, and mutations affect glycolytic capacity in skeletal
muscle, affecting glycogen levels due to metabolic accumulation.
Consequently, mutations/deficiency in this enzyme lead to
glycogen depletion/storage disease type X [14].
Prmt4 siRNA expression attenuates the expression of the mRNAs
encoding type IIB and IIX MyHCs (myosin heavy chains)
Interestingly, the proportion of type II/glycolytic muscle fibres in
the mouse displays a strong positive correlation with AMPKγ 3.
Moreover, type II fibres store increased levels of glycogen [28].
Therefore we examined the effect of Prmt4 siRNA transfection
on the expression of the predominantly expressed type IIB and
IIX MyHC isoforms (gold standard markers of type IIX/B fibres)
in skeletal muscle cells. We observed a significant decreased in
expression of the mRNAs encoding type IIB and IIX MyHCs, but
no change to type I MyHC mRNA in the Prmt4 siRNA-transfected
cells (Figures 5A, 5B and 5C respectively).
Stable expression of the methyltransferase and co-activator
(function)-deficient PRMT4 (VLD-AAA and E267Q) mutants
decreases expression of genes involved in glycogen metabolism
We explored further the association between PRMT4
expression and the regulation of genes involved in glycogen
metabolism. Skeletal muscle cells were transfected with
Figure 5 Prmt4 siRNA expression attenuates the expression of MyHC
type IIB and IIX expression
C2C12 cells were transfected with Prmt4 (si-Prmt4) and control (si-SS) siRNA for 48 h in
differentiation medium. Total RNA was analysed by qPCR for (A) MyHC (Myh) 4/Type IIb,
(B) MyHC1/Type IIX and (C) MyHC7/Type I. Results were normalized against 18S rRNA
(n = 3). Statistical significance was calculated using Student’s unpaired t test; **P < 0.01;
***P < 0.001.
vector only (i.e. pSG5), native PRMT4 expression plasmid,
and the two site-specific mutants in the SAM-binding
domain (that attenuates the methyltransferase and co-activator
actvities). A polyclonal pool of stable cells (to avoid clonal
variation) was isolated after 10–14 days of selection with
G418 (600 μg/ml). Cells were subsequently passaged and
plated (three replicates per cell line), harvested, total RNA
and protein were extracted and PRMT4 mRNA and protein
expression were measured by qRT-PCR and Western blot analysis.
We observed the increased expression of total (endogenous and
ectopic) Prmt4 mRNA in the native and site-specific PRMT4
mutants stable cells relative to the vehicle (pSG5) (Figure 6A).
This suggested increased ectopic expression of native and mutant
PRMT4 (relative to endogenous). Surprisingly, Western blot
analysis indicated only slight increases in protein expression
(Figure 6B). Several reports have demonstrated that differential
mRNA expression of proteins in heart muscle does not correlate
with protein expression [29,30]. Secondly, similar observations in
skeletal muscle cells have been observed by Raichur et al. [31],
that showed transfection of RORγ (retinoic acid-receptor-related
orphan receptor γ -subunit) expression vectors increased mRNA
expression in stables, but this did not translate into changes in
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S.-C.M. Wang and others
Figure 6 Expression of Prmt4 in stable expression of Prmt4 , and two sitespecific mutants VLD and E267Q
(A) Total RNA was analysed by qRT-PCR for Prmt4 expression in the polycolonal pool of
stably transfected C2C12 cells with control (SG5), PRMT4 and two PRMT4 mutants, VLD
and E267Q. Results were normalized against 18S rRNA (n = 3). Statistical significance was
calculated using one-way ANOVA and Tukey’s Multiple Comparison test ***P < 0.001. (B)
CARM1/PRMT4 protein expression in the stably transfected C2C12 cells with control (SG5),
PRMT4 and two PRMT4 mutants, VLD and E267Q, were analysed by Western blotting. GAPDH
was used as a loading control. An aliquot of 20 μg of total protein from cells was resolved per
lane.
protein expression, indicating total protein expression in muscle
is under tight control. We next measured the mRNA expression
of Gys1, Pgam2, Ampkγ 3 and Pygm (Figures 7A, 7B, 7C and 7D
respectively). We observed a significant decrease in the expression
of Gys1, Pgam2 and Ampkγ 3 (but not Pygm) after transfection
of the PRMT4 methyltransferase-deficient mutants (relative to
native CARM1 expression).
Recently, Ljubicic et al. [32] established links between
AMPK and PRMT4 in dystrophic muscle. Secondly, AMPKα2
(Prkaa2) has been demonstrated to regulate ERRα (oestrogenrelated receptor α) expression [33], and ERRγ expression
has been demonstrated to increase phospho-AMPK in cardiac
and skeletal muscle tissue [34]. Hence we examined, but
did not observe, changes in protein expression in total
and/or phospho-AMPKα in the stable PRMT4 gain and
loss of function cell lines (Supplementary Figure S2A at
http://www.BiochemJ.org/bj/444/bj4440323add.htm). No change
in Errα expression (Supplementary Figure S2B) was observed;
however, we did observe increased and decreased Errγ expression
in the PRMT4 gain- and loss-of-function transfected cells
respectively (Supplementary Figure S2C).
In summary, we have provided evidence indicating that PRMT4
expression was necessary for the expression of these genes
involved in glycogen metabolism, and that the mRNA expression
was dependent on the methyltransferase function of this coactivator.
Stable expression of the native PRMT4 and the methyltransferase
and co-activator (function)-deficient PRMT4 (VLD-AAA) mutant
increase and decrease glycogen levels
We utilized the vector-only (SG5), PRMT4 and the VLD
mutant to examine whether the selective attenuation of gene
expression involved in glycogen metabolism affected the
glycogen levels/stores in cells. Cells were harvested and
homogenized, and we used the Biovision glycogen assay kit that
c The Authors Journal compilation c 2012 Biochemical Society
Figure 7 Stable expression of the methyltransferase and co-activatordeficient PRMT4 mutants (VLD and E267Q) decreases expression of genes
involved in glycogen metabolism
Total RNA from the polyclonal pool of stably transfected C2C12 cells with control (SG5),
CARM1/PRMT4 and two PRMT4 mutants, VLD and E267Q, were analysed by qRT-PCR for (A)
Gys1, (B) Pgam2 , (C) Ampkγ 3 and (D) Pygm . Results were normalized against 18S rRNA
(n = 3), and statistical significance was calculated using one-way ANOVA and Tukey’s Multiple
Comparison test; *P < 0.05; ***P < 0.001.
Figure 8 Stable expression of PRMT4, and two site-specific mutants VLD
and E267Q, alters the level of glycogen content in C2C12 cells
Stable cells cultured in differentiation medium for 4 days after they reached confluency were
harvested in water and homogenized. Glycogen content was determined, and
results were expressed as μg of glucose/mg of protein. Statistical significance between each cell
line was calculated using one-way ANOVA and Tukey’s Multiple Comparison test; *P < 0.05;
***P < 0.001.
utilizes glucoamylase to hydrolyse glycogen to glucose, which
in turn is oxidized and the product generated is subsequently
assayed. In concordance, we observed that native and mutant
(methyltransferase-deficient VLD) PRMT4 induced an increase
and decrease in glycogen levels respectively (Figure 8). This is
consistent with the changes in gene expression, and provides
additional functional evidence for the role of PRMT4 in glycogen
metabolism. This is consistent with the literature, which suggests
that glycogen storage diseases are associated with aberrant
expression of enzymes involved in glycogen breakdown and
synthesis. For example, glycogen disorders (with mutation in
Pygm, that decrease glycogen breakdown) are associated with
decreased Gys1 expression and glycogen. For example, glycogen
storage disease type V/McArdle’s disease, with mutations
PRMT4 regulates glycogenic genes
in PYGM, also displays reduced GYS1 expression. This is in
concordance with the gene signature identified [15,18].
Common regulatory factors control the transcription of the
PRMT4-dependent genes regulating glycogen metabolism
We utilized MatInspector, and analysed the transcription factor
binding in the promoters of the three genes identified in this
study involved in glycogen metabolism: Pygm, Pgam2 and Gys1.
A total of 578 transcription-factor-binding sites were identified,
and 38 transcription-factor-binding sites were common to all
three promoters (Supplementary Figure S3 and Supplementary
Table S3 at http://www.BiochemJ.org/bj/444/bj4440323add.htm).
Out of the 38 transcription factors, greater than 50 % of these
have been reported to interact with CARM1/PRMT4 and/or
SRC1 (steroid receptor co-activator 1)–SRC3 that recruit
PRMT4 (Supplementary Table S3). For example, these include
CCAAT/enhancer-binding protein transcription factors, p53,
COUP-TF (chicken ovalbumin upstream promoter-transcription
factor), MyoD, etc. CCAAT/enhancer-binding proteins [35,36],
COUP-TFI/NR2F [37], p53 [38] and MyoD [39] have been
identified as important transcriptional regulators of glycogen
metabolism. Sibut et al. [35] determined that the CCAAT/
enhancer-binding proteins were a primary regulator of glycogen
content after profiling fat and lean chickens with increased and
decreased glycogen levels respectively in muscle tissue.
DISCUSSION
In the present study we have demonstrated that Prmt4 siRNA
expression selectively targets genes involved in glycogen
metabolism encoding Gys1, Pgam2 and Pygm, and not any other
genes/pathways involved in glucose homoeostasis (including
genes involved in glycolysis, gluconeogenesis, TCA and pentose
phosphate cycles). Moreover, there is no effect on several
other genes/signalling pathways involved in lipid and energy
homoeostasis. This was substantiated by qRT-PCR analysis of
>200 genes involved in fat, carbohydrate and energy metabolism,
and several signalling pathways, including PI3K/Akt, AMPK,
p38 MAPK, melanocortin, ROCK (Rho-associated kinase),
adrenergic, etc. Interestingly, the PRMT4-dependent glycogenic
metabolic footprint was associated with decreased expression
of the mRNAs encoding AMPK(α2/γ 3) (Prkaa2 and Prkag3
respectively) and p38 MAPK, and the type IIB/X MyHCs (the
major thick filament protein of the fast-twitch muscle fibres).
These links to kinase-dependent signalling pathways further
substantiate the selective and specific effects of PRMT4 lossof-function in skeletal muscle cells. Finally, the experiments
demonstrated that the methyltransferase domain of PRMT4 was
necessary for the expression of genes involved in glycogen
metabolism (and mRNAs encoding AMPKα2 and γ 3), and the
maintenance of glycogen levels.
The links with AMPKα2/γ 3 (Prkaa2 and Prkag3 respectively)
and p38 MAPK further substantiate the role of PRMT4 in
glycogen metabolism. For example, of the three genes involved
in glycogen metabolism, the two AMPKs (α2 and γ 3) and
p38 MAPK were the only (additional) genes differentially
expressed in a significant manner (>1.5-fold) from >200 qRTPCRs of critical genes that control, and are involved in,
metabolism. In humans, PGAM2, PYGM and GYS1 deficiency
causes glycogen storage diseases type X, type V/McArdle’s
disease and type 0 respectively [14,15,18]. Interestingly, Pgam2
is not a glycogenic enzyme, it belongs to the glycolytic
pathway. Pgam2 is the rate-limiting glycolytic enzyme, and
329
mutations modulating its activity affect glycolytic capacity in
skeletal muscle, impacting on glycogen levels due to metabolic
accumulation. Consequently, mutations/deficiency in this enzyme
lead to glycogen depletion/storage disease type X [14].
Moreover, Hampshire pigs and humans that carry natural
mutations in the AMPKγ 3 subunit display an increase in
glycogen levels in type II muscle and have been implicated
in glycogen disorders, Wolff–Parkinson–White syndrome and
Pompe Disease (glycogen storage disease type II) [17,19]. The
AMPKγ 3 and α2 subunits are members of a trimeric complex,
with AMPKα2 involved in glycogen sensing (and the regulation
of glycogen synthase) and p38 MAPK expression also associates
with glycogen levels [27]. As discussed above, Ljubicic et al.
[32] established links between AMPK and PRMT4 in dystrophic
muscle, and previous links between AMPK and ERRα and
ERRγ expression have been identified [33,34]. In this context
we examined, but did not find, changes in total and/or phosphoAMPK in the stable PRMT4 gain- and loss-of-function cell lines.
However, we observed modulation of ERRγ expression in the
PRMT4 gain- and loss-of-function transfected lines, suggesting
aberrant signalling between these pathways.
The analysis of the three promoters involved in the regulation of
glycogen levels revealed that a subset of 38 transcription-factorbinding sites were common to all three promoters (Supplementary
Figure S3 and Supplementary Table S3). These included
CCAAT/enhancer-binding protein, p53, COUP-TF, MyoD, etc.
[35–39], all identified as important transcriptional regulators
of glycogen metabolism. Microarray-mediated profiling [35]
identified the CCAAT/enhancer-binding protein as a significant
regulator of glycogen content and turnover (in muscle tissue).
In the present study, we have also observed significant downregulation of mRNAs encoding the fast-twitch/type II MyHC-IIB
and MyHC-IIX in Prmt4 siRNA-transfected cells, demonstrating
that PRMT4 is necessary for type II muscle fibres. The decrease
in proportion of type II fast-twitch fibres correlates with the
decreased AMPKγ 3 mRNA expression and is consistent with
reports that AMPKγ 3 expression is strongly concordant with the
proportion of fast-twitch type II fibres [40]. Interestingly, Batut
et al. [41] demonstrated that PRMT4 regulates type II glycolytic
fast fibre expression, in the absence of changes in type I oxidative
slow fibre formation in zebrafish muscle. The present study
is entirely consistent with these observations. We identified
a significant decrease in expression of the mRNAs encoding
type IIB and X MyHC, associated with no change to type I MyHC
mRNA in the Prmt4 siRNA-transfected cells.
PRMTs, in particular PRMT5 and PRMT4/CARM1, have been
implicated in the control of myogenesis through interactions with
myogenic genes such as MyoD, myogenin and Mef2, and are
involved in ATP-dependent chromatin remodelling in the latter
stages of muscle differentiation [42–44]. These observations
are compatible with our observations, in that expression of
MyHC markers, ERRγ expression and glycogen are phenotypic
markers associated with differentiated skeletal muscle cells.
Furthermore, stable transfection of two PRMT4 site-specific
(methyltransferase deficient) mutants (CARM1/PRMT4 VLD
and E267Q) significantly attenuated: (i) the expression of genes
involved in glycogen metabolism; (ii) AMPKγ 3 levels; and
(iii) glycogen levels (but not in the PRMT4 E267Q transfected
cells). These observations were consistent with the effects of the
CARM1/Prmt4 siRNA-transfected cells.
In the context of metabolism, previous reports demonstrating
that PRMT4 up-regulates the expression of enzymes PEPCK
and glucose 6-phosphatase in hepatocytes [11], and impaired
PRMT1 activity attenuates the metabolic branch of insulin
signalling involving IRS-2 and PI3K, suggests that PRMT4
c The Authors Journal compilation c 2012 Biochemical Society
330
S.-C.M. Wang and others
regulates selective aspects of glucose homoeostasis [12] in a
cell-specific manner. In conclusion, our study provides rigorous
evidence supporting the hypothesis that PRMT4 is necessary for
the selective control of a specific gene expression programme
associated with glycogen metabolism. Interestingly, the targets
identified are all implicated in human disorders of glycogen
metabolism, and are targets of a common set of transcriptional
regulators.
AUTHOR CONTRIBUTION
Shu-Ching Mary Wang performed transfections, produced the PRMT4 gain and loss of
function cell lines, conducted Western blot analysis, extracted/analysed RNA, performed
manual qPCR and glycogen assays, participated in the study design, data analysis and
interpretation, and preparation of the paper. Dennis Dowhan participated in the siRNA
design, study design, data analysis and interpretation, bioinformatic promoter analysis
and preparation of the paper. Natalie Eriksson conducted the TLDA qPCR assays and
analysis of the RNA from the siRNA transfected cells and mouse type I and II skeletal
muscle. George Muscat conceived the study, co-ordinated the study design, approach
and planning of experiments, participated in data analysis and interpretation, and was
involved in the preparation and editing of the paper prior to submission. All authors read
and approved the final paper prior to submission.
ACKNOWLEDGEMENTS
We thank Dr Michael Stallcup (USC Norris Comprehensive Cancer Center, University of
Southern California, Los Angeles, CA, U.S.A.) for pSG5-Prmt4, pSG5-Prmt4-VLD and
pSG5-Prmt4-E267Q expression plasmids.
FUNDING
This work was supported by the National Health and Medical Research Council (NHMRC)
of Australia [grant number 569742]. G.E.O.M. is a Principal Research Fellow of the
NHMRC, and Natalie Eriksson was a recipient of an Australian Postgraduate Award (APA).
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Received 21 November 2011/9 March 2012; accepted 19 March 2012
Published as BJ Immediate Publication 19 March 2012, doi:10.1042/BJ20112033
c The Authors Journal compilation c 2012 Biochemical Society
Biochem. J. (2012) 444, 323–331 (Printed in Great Britain)
doi:10.1042/BJ20112033
SUPPLEMENTARY ONLINE DATA
CARM1/PRMT4 is necessary for the glycogen gene expression programme
in skeletal muscle cells
Shu-Ching Mary WANG1 , Dennis H. DOWHAN1 , Natalie A. ERIKSSON and George E. O. MUSCAT2
Obesity Research Centre, Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland 4072, Australia
Figure S1 Expression of PRMT4, PRMT1, PRMT5 and PRMT6 in C2C12
skeletal muscle cells
Western blotting analysis of PRMT levels in C2C12 skeletal muscle cells at confluence (Confl),
during differentiation, myotube day 2 (MT2) and myotube day 5 (MT5). GAPDH was used as a
loading control. Total protein (20 μg) from cells were loaded.
Figure S2 Effect of PRMT expression on AMPKα phosphorylation levels
and ERR expression
(A) Western blotting analysis of AMPKα and phospho-AMPKα protein expression in the stably
transfected C2C12 cells with SG5 control, PRMT4 and the two PRMT4 mutants, VLD and E267Q.
GAPDH was used as a loading control. Total protein (20 μg) from cells was loaded. Total mRNA
from the polyclonal pool of stably transfected C2C12 cells with SG5 (control), Prmt4 and the two
Prmt4 mutants, VLD and E267Q, were analysed by qRT-PCR for expression of (B) Errα and (C)
Errγ . Results were normalized against Rplp0 (n = 3) and statistical significance calculated using
one-way ANOVA and Tukey’s Multiple Comparison test; *P < 0.05; **P < 0.01; ***P < 0.001.
1
2
These authors contributed equally to this study.
To whom correspondence should be addressed (email [email protected]).
c The Authors Journal compilation c 2012 Biochemical Society
S.-C.M. Wang and others
Figure S3
Transcription-factor-binding analysis for the mouse Pygm , Pgam2 and Gys1 promoters
A total of 578 transcription factor binding site matches were found in the three promoter sequences, and only binding sites for 38 different transcription factor families were in all three promoter
sequences (refer to Table S3 for the full TraFac nomenclature and related references). Analysis were perfomed using MatInspector [1].
c The Authors Journal compilation c 2012 Biochemical Society
PRMT4 regulates glycogenic genes
Table S1
Analysis of mouse metabolic gene expression on siPrmt4 (and control, siSS) in C2C12 cells
The P values are displayed, and adjusted P values of target genes are given before and after the application of FDR respectively. Genes with an adjusted P value of less than 0.05 (after FDR) are
differentially expressed in a significant manner. The relative quantification (RQ) linear and log10 denotes the calculated fold differences (between the target Prmt4 and the calibrator/reference control
siRNA) in a linear and log scale respectively, normalized against the median of the geNorm selected controls, Gusb , Hprt1 and Rplp0 . Bold indicates genes differentially expressed in a significant
manner. Analysis also includes the t value, the empirical Bayes moderated t statistic (a variant t test), and empirically moderated estimated of standard error. B value provides information about their
ranking (Integromics Technical Support, Joaquin Pandaero, personal communication).
Detector
Log10 RQ
Linear RQ
P value
Adjusted P value (FDR)
Adjusted significance
B value
t value
CT status
Acsl1-Mm00484217_m1
Arntl-Mm00500226_m1
Atp5a1-Mm00431960_m1
Bhlhe40-Mm00478593_m1
Cd63-Mm01966817_g1
Clock-Mm00455950_m1
Crem-Mm00516346_m1
Cry1-Mm00514392_m1
Cry2-Mm00546062_m1
Cybb-Mm01287743_m1
Dbp-Mm00497539_m1
Dcn-Mm00514535_m1
G6pc-Mm00839363_m1
Gabpa-Mm00484598_m1
Hk1-Mm00439344_m1
Itpr1-Mm00439917_m1
M6prbp1-Mm00482206_m1
Mfn1-Mm00612599_m1
Mfn2-Mm00500120_m1
Ndufb8-Mm00482663_m1
Nfe2l2-Mm00477784_m1
Nox1-Mm00549170_m1
Nox3-Mm01339132_m1
Nox4-Mm00479246_m1
Npas2-Mm00500848_m1
Nrf1-Mm00447996_m1
Per1-Mm00501813_m1
Per2-Mm00478113_m1
Per3-Mm00478120_m1
Pkm2-Mm00834102_gH
Pon1-Mm00599936_m1
Pprc1-Mm00521078_m1
Rarres2-Mm00503579_m1
Sdhb-Mm00458268_m1
Sgk1-Mm00441380_m1
Skp1a-Mm00495559_m1
Slc25a33-Mm00510425_m1
Steap4-Mm00475402_m1
Tfam-Mm00447485_m1
Uqcrc2-Mm00445961_m1
Yy1-Mm00456392_m1
6.12 × 10 − 03
− 9.19 × 10 − 2
− 6.02 × 10 − 2
− 0.130045
− 9.58 × 10 − 2
− 9.52 × 10 − 2
7.73 × 10 − 2
− 0.0814788
− 0.0726486
0.2693215
5.46 × 10 − 2
3.63 × 10 − 2
− 3.45 × 10 − 2
− 1.30 × 10 − 3
− 4.89 × 10 − 2
− 4.60 × 10 − 2
− 4.94 × 10 − 2
− 7.42 × 10 − 2
− 9.61 × 10 − 2
− 0.0808767
− 5.24 × 10 − 2
− 0.1377714
5.93 × 10 − 2
0.05900188
0.32029592
− 0.1055612
− 3.48 × 10 − 2
− 3.50 × 10 − 2
− 3.17 × 10 − 2
− 5.49 × 10 − 2
− 3.96 × 10 − 2
− 0.113689
− 0.7942175
− 6.91 × 10 − 2
− 0.1001426
− 0.0595036
− 6.22 × 10 − 2
9.06 × 10 − 2
− 6.55 × 10 − 2
− 5.84 × 10 − 2
− 5.95 × 10 − 2
0.98600486
1.23570411
1.14869836
1.34910253
1.24688925
1.2451619
0.83701961
1.20636516
1.18208464
0.53787146
0.88188758
0.91976253
1.08272485
1.00300815
1.11909564
1.11162229
1.12038921
1.18618854
1.24775383
1.20469394
1.12818214
1.37331889
0.87236271
0.87296759
0.47830408
1.27514974
1.0834756
1.08397639
1.07574291
1.13471763
1.09555861
1.29923884
6.22611979
1.172564
1.25933898
1.14684202
1.15401875
0.81168958
1.16285134
1.14393097
1.14684202
0.85058691
3.96 × 10 − 2
1.75 × 10 − 2
6.84 × 10 − 4
6.59 × 10 − 3
0.18497657
0.34374411
3.09 × 10 − 2
3.74 × 10 − 3
0.36611056
0.25560451
0.30140688
0.11586359
0.97210645
7.14 × 10 − 2
0.23119194
5.42 × 10 − 2
0.1117784
5.37 × 10 − 3
2.34 × 10 − 2
1.58 × 10 − 2
0.77324949
0.90632133
0.38056773
2.71 × 10 − 2
5.47 × 10 − 4
5.79 × 10 − 2
0.35820475
0.25188769
0.01168583
0.94080584
1.16 × 10 − 2
0.29848047
1.33 × 10 − 2
9.55 × 10 − 3
3.31 × 10 − 2
0.16012834
0.28304548
0.02765462
4.78 × 10 − 2
2.72 × 10 − 2
0.914380929
8.97 × 10 − 2
6.26 × 10 − 2
9.80 × 10 − 3
0.04722625
0.284071156
0.422314187
7.83 × 10 − 2
4.02 × 10 − 2
0.425479838
0.354548187
0.381191054
0.199285381
0.972106452
0.133484178
0.342801848
0.111034166
0.199285381
4.62 × 10 − 2
7.43 × 10 − 2
6.19 × 10 − 2
0.852557129
0.950532131
0.430642427
7.43 × 10 − 2
9.80 × 10 − 3
0.113218403
0.425479838
0.354548187
0.055832293
0.963205975
0.055832293
0.381191054
5.74 × 10 − 2
0.055832293
7.90 × 10 − 2
0.264827637
0.380342363
7.43 × 10 − 2
0.102764481
7.43 × 10 − 2
Non-significant
Non-significant
Non-significant
Significant
Significant
Non-significant
Non-significant
Non-significant
Significant
Non-significant
Non-significant
Non-significant
Non-significant
Non-significant
Non-significant
Non-significant
Non-significant
Non-significant
Significant
Non-significant
Non-significant
Non-significant
Non-significant
Non-significant
Non-significant
Significant
Non-significant
Non-significant
Non-significant
Non-significant
Non-significant
Non-significant
Non-significant
Non-significant
Non-significant
Non-significant
Non-significant
Non-significant
Non-significant
Non-significant
Non-significant
− 7.1784596
− 4.4519181
− 3.548548
9.09 × 10 − 2
− 2.4552895
− 6.0527237
− 6.6142616
− 4.1814874
− 1.8159769
− 6.6663166
− 6.3556062
− 6.5021466
− 5.5881629
− 7.2009632
− 5.0848088
− 6.263571
− 4.7914835
− 5.5515207
− 2.2238763
− 3.8717005
− 3.4396428
− 7.1472439
− 7.1926696
− 6.6976907
− 4.0340471
0.33831958
− 4.8622519
− 6.6484245
− 6.3422995
− 3.0995038
− 7.1981442
− 3.0915824
− 6.4936467
− 3.2477108
− 2.8733061
− 4.2544852
− 5.9123304
− 6.44699
− 4.0580788
− 4.6555467
− 4.0408455
− 0.1982195
2.75332106
3.4714948
7.37162807
4.43639661
1.53365774
− 1.044004
2.96279751
5.0679462
− 0.9922233
− 1.2813854
− 1.1503744
1.89515319
3.67 × 10 − 2
2.27422568
1.36021109
2.49491852
1.92297852
4.65846079
3.2081285
3.56220181
0.30396673
− 0.12369
− 0.9601245
− 3.0787702
7.72953188
2.44152948
1.01021818
1.29292659
3.85259539
7.80 × 10 − 2
3.85949425
1.15819564
3.72468865
4.052205
2.90586516
1.64515587
− 1.2006023
3.05977358
2.59786397
3.0733923
Valid
Valid
Valid
Valid
Valid
Valid
Valid
Valid
Valid
No detection
Valid
Valid
No detection
Valid
Valid
Valid
Valid
Valid
Valid
Valid
Valid
No detection
No detection
Valid
Valid
Valid
Valid
Valid
Valid
Valid
No detection
Valid
Valid
Valid
Valid
Valid
Valid
Valid
Valid
Valid
Valid
c The Authors Journal compilation c 2012 Biochemical Society
S.-C.M. Wang and others
Table S2
Analysis of mouse glucose and glycogen gene expressions in Prmt4 (and control) siRNA-transfected C2C12 cells with RT–PCR array
The P values of target genes are shown before and after the application of FDR respectively. Statistical analysis is as detailed in Table S1. Bold indicates genes that are differentially expressed in a
significant manner.
Detector
Log10 RQ
Linear RQ
P value
Adjusted P value (FDR)
Adjusted significance
B value
Acly
Aco1
Aco2
Agl
Aldoa
Aldob
Aldoc
Bpgm
Cs
Dlat
Dld
Dlst
Eno1
Eno2
Eno3
Fbp1
Fbp2
Fh1
G6pc
G6pc3
G6pdx
Galm
Gapdhs
Gbe1
Gck
Gpi1
Gsk3a
Gsk3b
Gys1
Gys2
H6pd
Hk2
Hk3
Idh1
Idh2
Idh3a
Idh3b
Idh3g
Mdh1
Mdh1b
Mdh2
Ogdh
Pck1
Pck2
Pcx
Pdha1
Pdhb
Pdk1
Pdk2
Pdk3
Pdk4
Pfkl
Pgam2
Pgk1
Pgk2
Pgm1
Pgm2
Pgm3
Phka1
Phkb
Phkg1
Phkg2
Pklr
Prps1
Prps1l1
Prps2
Pygl
− 7.83 × 10 − 2
− 0.147504698
− 3.51 × 10 − 2
− 0.130446331
− 6.42 × 10 − 2
0.159545898
− 4.82 × 10 − 2
− 0.077264366
2.61 × 10 − 2
− 2.01 × 10 − 3
− 1.10 × 10 − 2
− 0.115394832
− 7.02 × 10 − 2
− 2.91 × 10 − 2
− 0.104357065
− 0.485661726
− 7.02 × 10 − 2
− 3.11 × 10 − 2
0.384314961
1.40 × 10 − 2
− 1.10 × 10 − 2
− 0.132453198
− 3.71 × 10 − 2
− 2.71 × 10 − 2
− 0.449538127
− 1.81 × 10 − 2
− 0.101346765
− 9.23 × 10 − 2
− 0.219751897
− 3.01 × 10 − 2
− 8.53 × 10 − 2
− 2.91 × 10 − 2
− 0.20269353
7.63 × 10 − 2
3.61 × 10 − 2
− 2.91 × 10 − 2
− 3.81 × 10 − 2
− 6.12 × 10 − 2
− 4.82 × 10 − 2
0.127436031
1.30 × 10 − 2
− 9.83 × 10 − 2
− 0.330129562
0.151518431
− 0.108370798
− 9.53 × 10 − 2
− 3.21 × 10 − 2
− 1.81 × 10 − 2
0.290995662
0.042144199
− 6.02 × 10 − 3
− 4.72 × 10 − 2
− 0.361235995
− 2.71 × 10 − 2
− 5.22 × 10 − 2
− 6.72 × 10 − 2
− 8.13 × 10 − 2
5.52 × 10 − 2
− 1.10 × 10 − 2
− 6.02 × 10 − 3
− 0.131449765
9.03 × 10 − 3
5.82 × 10 − 2
1.51 × 10 − 2
− 0.832849655
5.82 × 10 − 2
− 0.222762197
1.197478705
1.404444876
1.08422687
1.350349946
1.159363791
0.692554734
1.117287138
1.194715135
0.941696017
1.004631674
1.025741121
1.30435207
1.175547906
1.069299999
1.271619166
3.059579387
1.175547906
1.074252648
0.412748058
0.968170696
1.025741121
1.356604327
1.089248656
1.064370182
2.815387168
1.042465761
1.262835451
1.236846673
1.658639092
1.071773463
1.217003514
1.069299999
1.594753377
0.838955775
0.920187651
1.069299999
1.091768265
1.15135548
1.117287138
0.7456997
0.970410231
1.25411241
2.138599997
0.705474904
1.283425898
1.245449622
1.076737568
1.042465761
0.511686946
0.907519155
1.01395948
1.114708637
2.29739671
1.064370182
1.127660927
1.167427804
1.205807828
0.880665874
1.025741121
1.01395948
1.353473524
0.979420298
0.87458267
0.965936329
6.805337288
0.87458267
1.670175839
1.74 × 10 − 3
4.32 × 10 − 3
0.396141905
8.69 × 10 − 2
4.53 × 10 − 2
0.221785934
0.529510551
1.16 × 10 − 2
0.54792719
0.914089758
0.740194938
1.03 × 10 − 2
4.04 × 10 − 2
0.566160984
2.80 × 10 − 3
0.180583311
0.637447393
0.256722655
0.577691777
0.638057275
0.70524468
0.010344968
0.778390779
0.296665159
0.621715349
0.347198015
1.90 × 10 − 2
0.100015297
2.24 × 10 − 3
0.890157893
8.50 × 10 − 2
0.137237308
0.155198303
1.86 × 10 − 2
0.223478654
0.138573796
0.205593
0.217797637
7.33 × 10 − 2
0.352610581
0.411850678
9.64 × 10 − 3
3.59 × 10 − 2
0.022522941
6.04 × 10 − 3
1.42 × 10 − 2
0.435387546
0.397063876
0.309287351
7.00 × 10 − 2
0.768796558
0.18271144
6.16 × 10 − 5
0.351233783
0.885095129
0.395277107
1.21 × 10 − 2
2.55 × 10 − 2
0.703824046
0.860540325
3.48 × 10 − 2
0.706791497
0.690860288
0.426072376
0.221922656
8.38 × 10 − 2
0.277756633
4.70 × 10 − 2
6.05 × 10 − 2
0.575058028
0.235453619
0.154671656
0.426641068
0.717401391
8.44 × 10 − 2
0.730569587
0.925102887
0.82901833
8.44 × 10 − 2
0.151315916
0.743086291
4.70 × 10 − 2
0.393532332
0.788188398
0.454941862
0.746555527
0.788188398
0.802303861
8.44 × 10 − 2
0.849153577
0.498397466
0.788188398
0.548505348
0.106168665
0.262540154
4.70 × 10 − 2
0.911869061
0.235453619
0.33257711
0.362129374
0.106168665
0.426641068
0.33257711
0.426641068
0.426641068
0.219961585
0.548505348
0.586363678
8.44 × 10 − 2
0.143779076
0.111289826
7.25 × 10 − 2
9.18 × 10 − 2
0.599550064
0.575058028
0.50941446
0.217736059
0.849153577
0.393532332
5.17 × 10 − 3
0.548505348
0.911869061
0.575058028
8.44 × 10 − 2
0.119014877
0.802303861
0.903567341
0.143779076
0.802303861
0.802303861
0.596501327
0.426641068
0.235453619
0.476154227
Significant
Non-significant
Non-significant
Non-significant
Non-significant
Non-significant
Non-significant
Non-significant
Non-significant
Non-significant
Non-significant
Non-significant
Non-significant
Non-significant
Significant
Non-significant
Non-significant
Non-significant
Non-significant
Non-significant
Non-significant
Non-significant
Non-significant
Non-significant
Non-significant
Non-significant
Non-significant
Non-significant
Significant
Non-significant
Non-significant
Non-significant
Non-significant
Non-significant
Non-significant
Non-significant
Non-significant
Non-significant
Non-significant
Non-significant
Non-significant
Non-significant
Non-significant
Non-significant
Non-significant
Non-significant
Non-significant
Non-significant
Non-significant
Non-significant
Non-significant
Non-significant
Significant
Non-significant
Non-significant
Non-significant
Non-significant
Non-significant
Non-significant
Non-significant
Non-significant
Non-significant
Non-significant
Non-significant
Non-significant
Non-significant
Non-significant
− 1.117115587
− 2.153172354
− 6.899905521
− 5.466406885
− 4.773106549
− 6.397164659
− 7.111665492
− 3.266033817
− 7.134046616
− 7.362321919
− 7.298052398
− 3.133481137
− 4.650983493
− 7.154869588
− 1.660502591
− 6.202459517
− 7.224781225
− 6.531286172
− 7.167384481
− 7.225306531
− 7.276409766
− 3.139383387
− 7.318157049
− 6.659542806
− 7.210823987
− 6.793279201
− 3.817334919
− 5.612040394
− 1.405134908
− 7.357461056
− 5.444030452
− 5.933062067
− 6.054959166
− 3.798958993
− 6.404233812
− 5.94273157
− 6.326130979
− 6.380252008
− 5.2884946
− 6.806064727
− 6.930249737
− 3.060527343
− 4.522787492
− 4.008576846
− 2.532466006
− 3.495882889
− 6.972656039
− 6.901734329
− 6.695597443
− 5.239392069
− 7.31344379
− 6.213734659
2.596754911
− 6.802837814
− 7.356276814
− 6.898184555
− 3.310887676
− 4.145996918
− 7.275461593
− 7.349750838
− 4.487776258
− 7.277435949
− 7.266554475
− 6.956292199
− 6.397738019
− 5.429177407
− 6.601708704
c The Authors Journal compilation c 2012 Biochemical Society
t value
5.916393088
4.828826396
0.924650439
2.107724556
2.623719516
− 1.38813253
0.673449087
3.817770126
− 0.64241898
0.11324654
0.349878591
3.931182411
2.715627363
0.612356053
5.325810686
1.546516053
0.500187415
1.274172357
− 0.593659232
− 0.499259953
0.399798739
3.926096253
0.296456066
1.159982082
0.524284691
1.033373254
3.362856618
1.999142834
5.597461053
0.14502882
2.124365096
1.756597687
1.662546842
− 3.377622023
− 1.382239724
1.749187078
1.446743114
1.402185023
2.239864009
− 1.020755154
− 0.892041011
3.994334197
2.812730528
− 3.210637783
4.468264033
3.624973629
0.844939882
0.922708731
− 1.126718562
− 2.276300254
0.309775268
1.537524371
11.76707208
1.023949499
0.151773412
0.926474986
3.779770514
− 3.102789681
0.40185151
0.18460869
2.839374914
− 0.397565854
− 0.420675979
− 0.86334225
1.387655007
− 2.135406197
1.212211079
CT status
Valid
Valid
Valid
Valid
Valid
Valid
Valid
Valid
Valid
Valid
Valid
Valid
Valid
Valid
Valid
Valid
Valid
Valid
No detection
Valid
Valid
Valid
Valid
Valid
No detection
Valid
Valid
Valid
Valid
No detection
Valid
Valid
Valid
Valid
Valid
Valid
Valid
Valid
Valid
Valid
Valid
Valid
No detection
Valid
Valid
Valid
Valid
Valid
No detection
Valid
Valid
Valid
Valid
Valid
No detection
Valid
Valid
Valid
Valid
Valid
Valid
Valid
No detection
Valid
No detection
Valid
Valid
PRMT4 regulates glycogenic genes
Table S2
Continued
Detector
Log10 RQ
Linear RQ
P value
Adjusted P value (FDR)
Adjusted significance
B value
t value
CT status
Pygm
Rbks
Rpe
Rpia
Sdha
Sdhb
Sdhc
Sdhd
Sucla2
Suclg1
Suclg2
Taldo1
Tkt
Tpi1
Ugp2
− 0.268920129
− 5.82 × 10 − 2
3.91 × 10 − 2
− 5.62 × 10 − 2
− 7.12 × 10 − 2
− 1.20 × 10 − 2
− 2.61 × 10 − 2
− 5.62 × 10 − 2
− 7.02 × 10 − 3
− 9.03 × 10 − 3
4.92 × 10 − 2
− 6.62 × 10 − 2
− 3.11 × 10 − 2
− 2.11 × 10 − 2
− 2.71 × 10 − 2
1.85746282
1.143402487
0.91383145
1.138131035
1.178267139
1.028113827
1.061913804
1.138131035
1.016304932
1.021012126
0.892959511
1.164733586
1.074252648
1.049716684
1.064370182
6.88 × 10 − 4
0.179988109
6.06 × 10 − 2
2.11 × 10 − 2
4.60 × 10 − 2
0.650819125
0.693152839
0.10334619
0.810641634
0.797985527
4.14 × 10 − 2
0.24074767
0.259966778
0.395707715
0.255731399
0.028878135
0.393532332
0.19584567
0.110586312
0.154671656
0.792301544
0.802303861
0.263063029
0.861948066
0.859369029
0.151315916
0.449395652
0.454941862
0.575058028
0.454941862
Significant
Non-significant
Non-significant
Non-significant
Non-significant
Non-significant
Non-significant
Non-significant
Non-significant
Non-significant
Non-significant
Non-significant
Non-significant
Non-significant
Non-significant
− 6.88 × 10 − 2
− 6.199278679
− 5.086893211
− 3.934333982
− 4.791473392
− 7.236034783
− 7.26816332
− 5.645725449
− 7.332399322
− 7.327100715
− 4.677421095
− 6.472883539
− 6.542604134
− 6.899042148
− 6.527792835
7.205412485
1.549049238
− 2.38952907
3.269425013
2.609941156
0.479962763
0.417334669
1.973936488
0.252115627
0.26944094
− 2.695684925
1.324384889
1.264324282
0.925566097
1.277203986
Valid
Valid
Valid
Valid
Valid
Valid
Valid
Valid
Valid
Valid
Valid
Valid
Valid
Valid
Valid
Table S3
TraFaC (transcription factor binding site comparison) nomenclature
List of TraFaC nomenclatures for the transcription factor families found in all three promoter sequences (Pygm , Pgam2 and Gys1), with related references that implicates Prmt4 interaction and/or
through interacting proteins SRC1–SRC3 and CBP [CREB (cAMP-response-element-binding protein)-binding protein]/p300. The related references are publications that implicate Prmt4/CARM1
interaction or association (through CARM1-interacting proteins SRC-1/SRC-2/SRC-3 or CBP/p300). AP, activating protein; bHLH, basic helix–loop–helix; BORIS, brother of the regulator of imprinted
sites; CTCF, CCCTC-binding factor; EGR, early growth-response gene product; EVI, ecotropic viral integration site; HAND, heart and neural crest derivatives expressed; HLH, helix–loop–helix; LEF,
lymphoid enhancer factor; MAF, mammary activating factor; PAR bZip, proline- and acid-rich basic region leucine zipper; PAX, paired box; RXR, retinoid X receptor; TCF, T-cell factor.
Family
Description
AP1R
CAAT
CREB
GREF
LHXF
NEUR
ETSF
MAZF
ZF02
AP4R
CART
CTCF
EVI1
HOMF
P53F
RXRF
STAT
HAND
HOXC
PARF
VTBP
MOKF
MYOF
DMRT
MYBL
NKXH
MYOD
PAX
E4FF
INSM
NR2F
SF1F
CLOX
GFI1
KLFS
EGRF
GLIF
LEFF
ZXFY
MAF and AP1-related factors
CAAT-binding transcription factor 1/nuclear factor 1
cAMP-responsive-element-binding proteins
Glucocorticoid-responsive and -related elements
Lim homeodomain factors
NeuroD/Beta2, HLH domain
Human and murine ETS1 factors
Myc-associated zinc fingers
C2H2 zinc finger transcription factors 2
AP4 and related proteins
Cart-1 (cartilage homeoprotein 1)
CTCF and BORIS gene family
EVI1-myleoid transforming protein
Homeodomain transcription factors
p53 tumour suppressor (negative regulator of the tumor suppressor Rb)
RXR heterodimer binding sites
Signal transducer and activator of transcription
bHLH transcription factor dimer of HAND2 and E12
HOX–PBX complexes
PAR bZIP family
Vertebrate TATA-binding protein factor
Mouse Krüppel-like factor
Myogenic factors
DM-domain-containing transcription factors
Cellular and viral myb-like transcriptional regulators
NKX/DLX homeodomain sites
Myoblast-determining factor
PAX-4/PAX-6 paired domain binding sites
Ubiquitous GLI (Krüppel-like zinc finger involved in cell cycle regulation)
Insulinoma-associated factors
Nuclear receptor subfamily 2 factors
Vertebrate steroidogenic factor
CLOX and CLOX homology (CDP) factors
Growth factor independence (transcriptional repressor)
Krüppel-like transcription factors
EGR/nerve growth factor induced protein C and related factors
GLI zinc finger family
LEF1/TCF
Zfx and Zfy (transcription factors implicated in mammalian sex determination)
Related reference
[2]
[3]
[4]
[5]
[6]
[7]
[8]
[9]
[10]
[11]
[12]
[13]
[14]
[15]
[16]
[17]
[18]
[19]
[10]
[20]
[21]
[22]
c The Authors Journal compilation c 2012 Biochemical Society
S.-C.M. Wang and others
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