J Appl Physiol 115: 1065–1074, 2013.
First published July 18, 2013; doi:10.1152/japplphysiol.00611.2013.
Time course of gene expression during mouse skeletal muscle hypertrophy
Thomas Chaillou,1,2 Jonah D. Lee,1,2,3 Jonathan H. England,1,2 Karyn A. Esser,1,2 and John J. McCarthy1,2
1
Center for Muscle Biology, University of Kentucky, Lexington, Kentucky; 2Department of Physiology, College of Medicine,
University of Kentucky, Lexington, Kentucky; and 3Department of Rehabilitation Sciences, College of Health Sciences,
University of Kentucky, Lexington, Kentucky
Submitted 21 May 2013; accepted in final form 15 July 2013
transcriptome; branched-chain amino acid; KLF15; ILK signaling;
micro-RNA-1
is primarily dictated by the net
balance between the rates of protein synthesis and degradation.
It is well established that muscle hypertrophy is the result of a
sustained, net increase in the rate of protein synthesis that
ultimately leads to the accretion of myofibrillar protein (7).
Over the last decade, great strides have been made in defining
the signaling pathways that control protein synthesis, as exemplified by the identification of the insulin-like growth factor
I/Akt/mechanical target of rapamycin (mTOR) complex 1
(mTORC1) signaling pathway and its central role in the regulation of muscle hypertrophy (1). Although gene expression
during skeletal muscle hypertrophy has been well studied, to
our knowledge, only a few studies using microarray technology have performed a limited temporal (two time points)
transcriptome analysis (16, 17, 22, 26, 29). Studies using
ADULT SKELETAL MUSCLE MASS
Address for reprint requests and other correspondence: T. Chaillou, Univ. of
Kentucky, Dept. of Physiology, 800 Rose St., MS508, Lexington, KY 405360298 (e-mail: [email protected]).
http://www.jappl.org
microarray analysis are important because they provide an
unbiased analysis of the biological processes that are operative
during muscle hypertrophy, thereby allowing for the identification of genes and pathways not previously recognized as
having a role in skeletal muscle growth. The purpose of this
study was to perform a comprehensive transcriptome analysis
by collecting data at multiple time points during the hypertrophic response induced by synergist ablation. We used this
model of muscle hypertrophy because it induces a robust and
progressive increase in muscle mass (18, 20, 30). The primary
goal of performing a time course analysis was to identify
signaling pathways that are operative throughout the hypertrophic response. In addition, we view this annotated transcriptome data set as a valuable resource for researchers with an
interest in skeletal muscle plasticity.
The global gene expression patterns were determined using
a microarray analysis after 1, 3, 5, 7, 10, and 14 days of
functional overload in mouse plantaris muscle. We used two
parameters, principal component analysis (PCA) of gene expression and the number of differentially expressed genes, to
define three gene expression patterns of the hypertrophic response: early (1 day), intermediate (3, 5, and 7 days), and late
(10 and 14 days) patterns. Moreover, the analysis of the
canonical pathways revealed the involvement of specific pathways at each gene expression pattern in response to mechanical
overload. We identified 1) several pathways associated with
the immune response and injury/disease during the early gene
expression pattern; 2) a number of pathways related to metabolism, mechanotransduction, and mitochondrial function during the intermediate gene expression pattern; and 3) one
pathway related to blood coagulation during the late gene
expression pattern. In particular, we identified valine degradation and integrin-linked kinase (ILK) pathways as potentially
important to cell growth, although little is known about their
possible role in skeletal muscle hypertrophy. The informatics
analysis led to the identification of the transcription factor
Klf15 (Kruppel-like factor-15) and the micro-RNA-1 (miR-1)
as two potential upstream regulators of valine degradation and
ILK pathways, respectively.
MATERIAL AND METHODS
Animal Care and Use
All experimental procedures performed in this study were approved
by the University of Kentucky Institutional Animal Care and Use
Committee. Male C57BL/6J mice (The Jackson Laboratory, Bar
Harbor, ME), 5 mo of age, were housed in a temperature- and
humidity-controlled facility on a 14:10 h light-dark cycle with access
to food and water ad libitum. The bilateral synergist ablation model
was used to induce hypertrophy of the plantaris muscle, as previously
described (19). Briefly, a small incision was made on the dorsal aspect
of the lower hindlimb of a continually anesthetized mouse (2%
8750-7587/13 Copyright © 2013 the American Physiological Society
1065
Downloaded from http://jap.physiology.org/ by 10.220.33.4 on June 14, 2017
Chaillou T, Lee JD, England JH, Esser KA, McCarthy JJ. Time
course of gene expression during mouse skeletal muscle hypertrophy.
J Appl Physiol 115: 1065–1074, 2013. First published July 18, 2013;
doi:10.1152/japplphysiol.00611.2013.—The purpose of this study
was to perform a comprehensive transcriptome analysis during skeletal muscle hypertrophy to identify signaling pathways that are
operative throughout the hypertrophic response. Global gene expression patterns were determined from microarray results on days 1, 3, 5,
7, 10, and 14 during plantaris muscle hypertrophy induced by synergist ablation in adult mice. Principal component analysis and the
number of differentially expressed genes (cutoffs ⱖ2-fold increase or
ⱖ50% decrease compared with control muscle) revealed three gene
expression patterns during overload-induced hypertrophy: early (1
day), intermediate (3, 5, and 7 days), and late (10 and 14 days)
patterns. Based on the robust changes in total RNA content and in the
number of differentially expressed genes, we focused our attention on
the intermediate gene expression pattern. Ingenuity Pathway Analysis
revealed a downregulation of genes encoding components of the
branched-chain amino acid degradation pathway during hypertrophy.
Among these genes, five were predicted by Ingenuity Pathway Analysis or previously shown to be regulated by the transcription factor
Kruppel-like factor-15, which was also downregulated during hypertrophy. Moreover, the integrin-linked kinase signaling pathway was
activated during hypertrophy, and the downregulation of musclespecific micro-RNA-1 correlated with the upregulation of five predicted targets associated with the integrin-linked kinase pathway. In
conclusion, we identified two novel pathways that may be involved in
muscle hypertrophy, as well as two upstream regulators (Kruppel-like
factor-15 and micro-RNA-1) that provide targets for future studies
investigating the importance of these pathways in muscle hypertrophy.
1066
Transcriptome Analysis During Skeletal Muscle Hypertrophy
isoflurane at 0.5 l/min), and the entire soleus was carefully removed
along with the majority of the gastrocnemius muscle. Particular
attention was made to ensure that the neural and vascular supply of the
plantaris muscle was not damaged during the excision of the synergist
muscles. Following recovery from surgery, mice were anesthetized at
the designated time point by an intraperitoneal injection of ketamine
(100 mg/kg) and xylazine (10 mg/kg), and plantaris muscles were
excised, weighed, placed in RNAlater (Ambion, Austin, TX), and
stored at 4°C until use. Plantaris muscle was collected at 1, 3, 5, 7, 10,
and 14 days after the surgery (d1, d3, d5, d7, d10, and d14, respectively; n ⫽ 6 per group). Control plantaris muscle (n ⫽ 6) was
collected from mice subjected to a sham synergist ablation surgery.
Following collection of the plantaris muscle, mice were killed by
cervical dislocation under anesthesia.
RNA Isolation
Microarray and Microarray Data Analysis
The microarray hybridization and processing were performed at the
University of Kentucky Microarray Core Facility, according to the
manufacturer’s protocol (Affymetrix, Santa Clara, CA). For each time
point, two Affymetrix chips (mouse gene 1.0 ST) were used with 250
ng of total RNA derived from a pooled sample of either the right or
left plantaris muscles from six animals. We pooled RNA samples
based on the experimental results reported by Kendziorski et al. (12),
showing that gene expression from RNA pools are similar to averages
of individuals that comprise the pool. The mouse gene 1.0 ST chip
provides coverage of 28,000 protein coding transcripts and 7,000
noncoding transcripts of which ⬃2,000 are long intergenic noncoding
transcripts. Then the gene expression obtained from the two chips
(replicates of the pooled RNA samples from the same animals, with
each pooled sample derived from separate plantaris muscles) at each
time point was averaged and uploaded to Partek Genomics Suite (St.
Louis, MO) to identify differentially expressed genes; the criteria for
a gene to be considered differentially expressed was a twofold or
greater increase or a ⱖ50% decrease in expression in the experimental
group relative to the control group. At this step, we did not set a lower
cutoff for the signal intensity to avoid excluding low expressed genes
that might show a significant upregulation in response to functional
overload. All of the microarray data obtained in this study are
available by using GEO accession number GSE47098 (available at
http://www.ncbi.nlm.nih.gov/geo). Ingenuity Pathway Analysis (IPA;
Ingenuity Systems, Redwood, CA) was then used to identify those
pathways that were significantly enriched in the list of differentially
expressed genes associated with skeletal muscle. The IPA program is
based on the Ingenuity Knowledge Base, which consists of millions of
relationships (between genes, protein complexes, and networks) extracted from peer-reviewed scientific literature (2). As miR-1 was
identified by IPA to target some upregulated transcripts associated
with ILK signaling (see RESULTS), we expanded our analysis and
confirmed this prediction by using the online miRNA database TargetScan 6.2 (14).
Chaillou T et al.
cDNA Synthesis and RT-PCR
Reverse transcription was performed using 1 ␮g of total RNA (n ⫽ 6 at
each time point) with oligo(dT) primer and Superscript III reverse transcriptase (Invitrogen, Carlsbad, CA), according the manufacturer’s instructions.
Taqman probe and primers for quantitative PCR (qPCR) reactions were
obtained from Applied Biosystems as follows: Klf15, Mm00517792_
m1; Bcat2, Mm00802192_m1; Ehhdah, Mm00619685_m1; Parva,
Mm00480444_m1; Snai2, Mm00441531_m1; Gapdh, Mm99999915_g1;
Rpl38, Mm03015864_g1; Ddit4, Mm00512504_g1. qPCR was performed
using Taqman Gene Expression Master Mix (2⫻) (Invitrogen, Carlsbad, CA), using 5 ␮l of diluted cDNA (1/10 dilution from stock
cDNA mixture) and 1 ␮l of primer mix in a 20-␮l final volume. qPCR
were performed using an ABI 7500 RT-PCR system (Invitrogen).
miR-1 detection. Reverse transcription was performed with 20 ng
of total RNA (n ⫽ 6 at each time point) using the miRCURY LNA
Universal RT micro-RNA PCR kit (Exiqon, Woburn, MA), according
to the manufacturer’s instructions. A synthetic spike-in template
(UniSp6) was added during the RT step to control for the efficiency of
cDNA synthesis and micro-RNA qPCR. qPCR was carried out with
Power SYBR Green PCR Master Mix (2⫻) (Invitrogen) using 4 ␮l of
diluted cDNA (1/40 dilution from stock cDNA mixture) and 2 ␮l of
primer mix from Exiqon (miR-1, no. 204344; UniSp6, no. 203450) in
a 20-␮l final volume. qPCR were performed using ABI 7500 RT-PCR
system according to Exiqon’s instructions.
mRNA and micro-RNA quantification. Quantification cycles (Cq)
were determined by ABI 7500 software version 2.0.1. Absolute
quantification was achieved by exponential conversion of the Cq
using the qPCR efficiency. qPCR efficiency was estimated from
standard curves obtained by serial dilutions (1-log range) of a pooled
sample for each RT set (23). Relative quantification of the target
mRNA was obtained after the normalization with the geometric mean
of exponential conversion of the Cq of three reference genes (Rpl38,
Gapdh, and Ddit4). The geometric mean based on these genes was not
affected by mechanical overload over time. Relative quantification of
the miR-1 was obtained after the normalization with UniSp6. We
chose to use this external normalization method because we were
unable to identify a constant reference gene for internal normalization
(data not shown).
Statistical Analysis
A right-tailed Fisher’s exact test was used to determine the top
statistically significant canonical pathways from IPA. For this analysis, a P value ⬍ 0.001 was considered statistically significant. The
total RNA concentration and qPCR data are presented as means ⫾ SE.
Multigroup comparisons were performed by one-way ANOVA followed by Tukey’s post hoc test. For the ANOVA analysis, the level of
statistical significance was set at P ⬍ 0.05.
RESULTS
Skeletal Muscle Hypertrophy Is Associated With Increased
Total RNA Content
In an effort to perform a comprehensive transcriptome analysis of adult skeletal muscle during hypertrophic growth, we
carried out a time course of 1, 3, 5, 7, 10, and 14 days
following synergist ablation. As our laboratory previously
reported, there was a significant and progressive increase in
plantaris muscle mass during mechanical overload (20). The
total RNA content progressively increased between 3 and 7
days of hypertrophy (0.86 ⫾ 0.07, 1.54 ⫾ 0.12, and 2.36 ⫾
0.14 ␮g/mg muscle after 3, 5, and 7 days of hypertrophy,
respectively) and then stabilized through d14.
J Appl Physiol • doi:10.1152/japplphysiol.00611.2013 • www.jappl.org
Downloaded from http://jap.physiology.org/ by 10.220.33.4 on June 14, 2017
Total RNA was prepared from plantaris muscle using TRIzol
reagent (Invitrogen, Carlsbad, CA) according to the manufacturer’s
directions. RNA samples were treated with TURBO DNase (Ambion,
Austin, TX) to remove genomic DNA contamination. The total RNA
concentration and purity were assessed by measuring the optical
density (230, 260, and 280 nm) with the Nanodrop 1000 Spectrophotometer (ThermoFisher Scientific, Wilmington, DE). RNA integrity
was assessed using the Agilent 2100 Bioanalyzer (Agilent Technologies, Palo Alto, CA); the average RNA integrity number value for all
samples was 9.46 ⫾ 0.10 (scale 1–10), indicating high-quality RNA
with minimal degradation products.
•
Transcriptome Analysis During Skeletal Muscle Hypertrophy
PCA Mapping (68.2%)
80
52
day 7
day 14
day 10
control
24
day 5
PC #2 24.5%
-3
day 3
-31
-60
-87
-115
-143
-171
day 1
-200
-140
-100
-61
-22
16
55
94
133
172
211
250
PC #1 43.7%
Fig. 1. A two-dimensional principal component analysis (PCA) mapping of the
variance in gene expression during hypertrophy. The distribution of each group
along the first component (x-axis, PC #1) suggested that the imposition of
synergist ablation had the greatest effect on gene expression variability
(43.7%). The distribution of groups along the second component (y-axis, PC
#2) indicated that the length of time that synergist ablation was applied had the
second greatest influence on the variability of gene expression (24.5%).
Down-regulated
Up-regulated
1600
1200
800
400
0
d1
d3
d5
d7
d10
d14
Fig. 2. Number of differentially expressed genes in response to mechanical
overload. The genes initially selected by Partek Genomics Suite from a twofold
increase or a 50% decrease in gene expression compared with control nonoverloaded muscle were then uploaded in Ingenuity Pathway Analysis software. This software filtered the mapped genes, which were associated with
skeletal muscle analysis. d1– d14, Days 1–14 after surgery, respectively.
whereas the proportion of up- and downregulated genes was
similar between d3 and d7. Taken together, the results from the
PCA and the pattern of change in the number of differentially
expressed genes provide parameters to assign three gene expression patterns for the hypertrophic response during mechanical
overload as: early (1 day), intermediate (3, 5, and 7 days), and late
(10 and 14 days) patterns.
Canonical Pathways Identified During the Early,
Intermediate, and Late Gene Expression Patterns of Skeletal
Muscle Hypertrophy
Using the differential gene expression lists, we applied IPA
to determine the enriched canonical pathways in skeletal muscle during mechanical overload. The number of canonical
pathways that were significantly (P ⬍ 0.001) enriched was the
highest at d1 (29) and then progressively decreased at each
subsequent time point (20, 19, 13, 11, and 9 at d3, d5, d7, d10,
and d14, respectively). In Fig. 3, we only presented the enriched pathways specific to each gene expression pattern
(early, intermediate, and late) and those common across the
entire time course. There were four pathways that were enriched across all three gene expression patterns of the time
course: hepatic fibrosis/hepatic stellate cell activation, T-helper
cell differentiation, LPS/IL-1-mediated inhibition of retinoid X
receptor (RXR) function and liver X receptor (LXR)/RXR
activation. The exact role of these pathways in the hypertrophic
response in skeletal muscle is not readily apparent, as they
have been primarily shown in the liver to regulate the production of connective tissue (hepatic fibrosis/hepatic stellate cell
activation and T-helper cell differentiation) (10, 11), cholesterol metabolism (LPS/IL-1 mediated inhibition of RXR function and LXR/RXR activation) (3, 34), and inflammation
(T-helper cell differentiation) (11).
Thirteen pathways were specifically enriched during the
early gene expression pattern of hypertrophy, in which the
majority of genes were upregulated. Most of these pathways
were categorized as the immune response (7) and disease (3),
while two nuclear receptor (peroxisome proliferator-activated
receptor signaling and vitamin D receptor/RXR activation) and
one cellular stress-related (p38 MAPK signaling) signaling
pathways were activated. The activation of the immune system
as represented by pathways such as inducible nitric oxide
synthase, IL-6, and nuclear factor-␬B signaling more than
J Appl Physiol • doi:10.1152/japplphysiol.00611.2013 • www.jappl.org
Downloaded from http://jap.physiology.org/ by 10.220.33.4 on June 14, 2017
To obtain a more global perspective on the variance in gene
expression across the time course, PCA was performed using
Partek Genomics Suite. PCA revealed that 68% of the variation
in gene expression was accounted for by the first two principal
components (Fig. 1). The distribution of each group along the
first component (x-axis, PC #1) indicated the imposition of
synergist ablation had the greatest effect on gene expression
variability. This is clearly shown by the distinct separation of
the control group from all of the experimental groups. The
distribution of groups along the second component (y-axis, PC
#2) suggested that the length of time that synergist ablation was
applied had the second greatest influence on the variability of
gene expression. Thus PCA provided evidence that the two
independent variables (synergist ablation and time) were the
primary source of variability in gene expression. The clustering
of the experimental groups along PC #2 supports the concept of
an early (d1), intermediate (d3–7), and late (d10 –14) gene
expression patterns of the hypertrophic response.
Next, we used IPA software to filter the differentially expressed genes (cutoffs ⬎2-fold or ⬍50%) associated with
skeletal muscle. As shown in Fig. 2, ⬃1,050 genes were
differentially expressed at d1, a number that progressively increased from d3 to reach ⬃1,700 genes at d7 and then decreased,
becoming the lowest at d14. The proportion of upregulated genes
was higher than that of downregulated genes at d1, d10, and d14,
1067
Chaillou T et al.
2000
Number of genes
Identification of Specific Gene Expression Patterns During
Skeletal Muscle Hypertrophy
•
1068
Transcriptome Analysis During Skeletal Muscle Hypertrophy
Category
Canonical pathway
Ce l lu lar s tre ss an d injury
Disease specific pathway
Immune response
Nuclear receptor signaling
•
Chaillou T et al.
d1
d3
↑
↓
p38 MAPK Signaling
21
7
Hepatic Cholestasis
Role of Macrophages, Fibroblasts and Endothelial Cells in Rheumatoid
Arthritis
Type I Diabetes Mellitus Signaling
26
4
42
3
19
1
Activation of IRF by Cytosolic Pattern Recognition Receptors
11
1
Acute Phase Response Signaling
25
2
IL-6 Signaling
28
1
0
iNOS Signaling
12
Macropinocytosis Signaling
12
2
MIF-mediated Glucocorticoid Regulation
9
2
NF-κB Signaling
31
3
PPAR Signaling
24
2
VDR/RXR Activation
15
4
d5
↑
↓
d7
↑
↓
↑
↓
Cel lul ar f unc tion a nd mainte na nc e
Mitochondrial Dysfunction
5
39
6
36
6
39
Cellular growth, development
I L K S ig na l i n g
26
10
32
10
33
11
Valine Degradation I
0
10
0
11
0
11
TCA Cycle II (Eukaryotic)
0
10
0
11
0
10
Metabolism
d10
↑ ↓
d14
↑ ↓
20
3
Intrinsic Prothrombin Activation Pathway
4
3
4
3
Disease specific pathway
Hepatic Fibrosis / Hepatic Stellate Cell Activation
26
7
28
6
30
8
31
8
26
6
25
1
Im m un e r e sp o ns e
T H e lp e r C e l l D if f e r e n t i a t i o n
17
0
17
1
15
1
14
2
12
1
11
1
LPS/IL-1 Mediated Inhibition of RXR Function
21
12
25
20
23
25
20
28
15
19
11
14
LXR/RXR Activation
22
5
23
8
19
11
18
15
14
11
11
10
Nuclear receptor signaling
Early pattern
Late pattern
Intermediate pattern
Common
Fig. 3. Canonical pathways associated with skeletal muscle in response to mechanical overload. The top statistically significant canonical pathways were
identified from IPA, using a right-tailed Fisher’s exact and a P value ⬍0.001 [-log(P value) ⬎ 3]. The pathways presented represent the pathways specific to
the early (d1), intermediate (d3– d7), and late (d10 – d14) gene expression patterns, and those pathways identified across all three patterns. The numbers represent
the numbers of either upregulated (1) or downregulated (2) genes associated with a specific pathway. iNOS, inducible nitric oxide synthase; PPAR, peroxisome
proliferator-activated receptor signaling; VDR, vitamin D receptor; RXR, retinoid X receptor; ILK, integrin-linked kinase; LXR, liver X receptor.
likely reflects the acute inflammatory response to muscle injury
resulting from mechanical overload (13). However, the prolonged upregulation of T helper cell differentiation pathway
across the entire time course suggests that the immune response is still active even after the initial muscle injury has
been repaired. The importance of the T-helper cell differentiation pathway to muscle hypertrophy remains to be understood.
During the intermediate gene expression pattern (d3– d7),
metabolic pathways involving mitochondria function (mitochondrial dysfunction) and energetic metabolism (TCA cycle
and valine degradation) were enriched. Noteworthy, most of
the genes related to these metabolic pathways were downregulated. In contrast, at this same time period, the genes associated
with the ILK signaling pathway were generally upregulated.
Only intrinsic prothrombin activation pathway was specifically
enriched during the late gene expression pattern (d10 – d14) of
hypertrophy. This pathway related to blood coagulation, which
may reflect a late adaptation of blood vessels/capillarity in
response to muscle injury, is not clearly understood during
hypertrophic growth.
Given the progressive increase in total RNA content observed between 3 and 7 days following synergist ablation,
certainly reflecting an increase in ribosome content associated
with increased protein synthesis (20), and the robust change in
gene expression during this time period (Fig. 2), we chose to
focus on the pathways enriched during the intermediate gene
expression pattern of hypertrophy. Surprisingly, we did not identify any enriched signaling pathways directly related to mTORC1,
a central regulator of protein synthesis during skeletal muscle
hypertrophy. We focused our attention on valine degradation and
ILK pathways. These two pathways stood out because ILK
signaling regulates gene expression via mechanotransduction, and
valine degradation is involved in branched-chain amino acid
(BCAA) availability, two processes potentially involved in muscle growth (24, 35).
Valine Degradation Pathway
IPA software specifically identified, during the intermediate
gene expression pattern of hypertrophy, the BCAA pathway
related to valine degradation. The two other pathways associated with BCAA degradation, the isoleucine and leucine degradation pathways, were not significant at P ⬍ 0.001, but were
significantly downregulated at a P ⬍ 0.05 at d3 for leucine
degradation pathway and d3 through d7 for isoleucine degradation pathway. These three BCAA degradation pathways are
characterized by a few common enzymatic complexes: BCAA
transaminase catalyzes the first enzymatic reaction of the three
BCAA degradation pathways, while two other enzymatic complexes (2-methylacyl-CoA dehydrogenase and enol-CoA hydratase) are common to both valine and isoleucine degradation
pathways.
As presented in Table 1, 11 genes were downregulated at
least one time between d3 and d7 of mechanical overload.
These genes exclusively encoded components of enzymatic
complexes (dehydrogenase, transaminase, hydrolase and hydratase) that are present at the different steps of valine degradation pathway (Fig. 4). Moreover, six of these genes (Auh,
Bcat2, Dld, Ehhadh, Hadha, and Hadhb) were also components of the isoleucine degradation pathway, while Auh and
Bcat2 were part of the leucine degradation pathway. Two other
J Appl Physiol • doi:10.1152/japplphysiol.00611.2013 • www.jappl.org
Downloaded from http://jap.physiology.org/ by 10.220.33.4 on June 14, 2017
C e l l u la r s t r es s an d in j ur y
Transcriptome Analysis During Skeletal Muscle Hypertrophy
•
1069
Chaillou T et al.
Table 1. Differentially regulated genes involved in the valine degradation pathway
Fold Change
Type
Symbol
Enzyme
BCAT2*†
Branched-chain amino acid transaminase 2
BCKDHA
BCKDHB
DBT
DLD*
Branched-chain keto acid dehydrogenase E1, ␣-polypeptide
Branched-chain keto acid dehydrogenase E1, ␤-polypeptide
Dihydrolipoamide branched-chain transacylase E2
Dihydrolipoamide dehydrogenase
AUH*†
EHHADH*
HADHA*
AU RNA binding protein/enoyl-CoA hydratase
Enoyl-CoA, hydratase/3-hydroxyacyl CoA dehydrogenase
Hydroxyacyl-CoA dehydrogenase/3-ketoacyl-CoA thiolase/
enoyl-CoA hydratase, ␣-subunit
Hydroxyacyl-CoA dehydrogenase/3-ketoacyl-CoA thiolase/
enoyl-CoA hydratase, ␤-subunit
Enoyl-CoA hydratase
HIBCH
3-Hydroxyisobutyryl-CoA hydrolase
HIBADH
3-Hydroxyisobutyrate dehydrogenase
HADHB*
Gene Name
Member of
d3
d5
d7
Branched-chain amino acid transaminase
0.48
0.51
0.51
2-Oxoisovalerate dehydrogenase
0.38
0.35
0.46
0.36
0.38
0.36
0.48
0.42
0.39
0.38
0.49
0.43
0.35
0.49
0.51
0.40
0.46
0.52
0.38
0.42
0.49
0.34
0.37
0.35
3-Hydroxyisobutyryl-CoA hydrolase
0.40
0.42
0.43
3-Hydroxyisobutyryl-CoA dehydrogenase
0.47
0.50
0.49
genes specifically associated with leucine degradation pathway
(Acadm and Mcc2) were also downregulated between d3 and
d7 (data not shown). These results clearly suggest that the
BCAA degradation pathway is significantly downregulated
during the intermediate gene expression pattern of the hypertrophic response.
The downregulation of the BCAA degradation pathway suggested expression of pathway components was coordinated
Substrate
through the use of a common upstream regulator. In support of
this idea, IPA revealed that the components of both the valine and
isoleucine degradation pathway Ehhadh, Hadha, and Hadhb, and
the component of leucine degradation pathway Acadm were all
potential targets of the transcription factor Klf15. Consistent with
such a mechanism, microarray analysis showed that Klf15
mRNA expression was decreased by ⬃60% between d3 and d7
of overload compared with control muscles, similar to the
Enzymatic complex
Gene
Branched-chain-aminoacid transaminase
BCAT2
2-oxoisovalerate
dehydrogenase
BCKDHA
BCKDHB
DBT
DLD
L-valine
2-oxoisovalerate
Decreased expression in Ov muscle
Decreased expression in Ov muscle
and predicted target of KLF15
Isotyryl-CoA
2-methylacyl-CoA
dehydrogenase
Methylacrylyl-CoA
Enoyl-CoA
hydratase
AUH
EHHADH
HADHA
HADHB
3-hydroxyisobutyrylCoA hydrolase
HIBCH
3-hydroxyisobutyryl-CoA
dehydrogenase
HIBADH
3-hydroxyisobutyryl-CoA
Fig. 4. Schematic representation of the valine
degradation pathway from the differentially regulated genes in response to 3–7 days of mechanical overload. This pathway was adapted from
the pathway determined by Ingenuity Pathway
Analysis. The genes bolded are predicted targets
of KLF15 (Kruppel-like factor-15). The gene
expression determined by microarray and the
gene names are presented in Table 1.
3-hydroxyisobutyrate
3-amino-2-methyl
propionate transaminase
Methylmalonatesemialdehyde
3-amino-2methylpropanoate
Methylmalonate-semialdehyde
dehydrogenase
Propanoyl-CoA
J Appl Physiol • doi:10.1152/japplphysiol.00611.2013 • www.jappl.org
Downloaded from http://jap.physiology.org/ by 10.220.33.4 on June 14, 2017
The nos. represent the fold change of gene expression after 3, 5 and 7 days (d3, d5, d7, respectively) of mechanical overload compared with control
non-overloaded muscles. Italicized numbers mean that the fold change in gene expression is lower than 50% decrease. The selected genes were differentially
expressed (twofold increase or 50% decrease) at least one time between d3 and d7. *Genes also associated with the isoleucine degradation pathway. †Genes also
associated with the leucine degradation pathway.
1070
Transcriptome Analysis During Skeletal Muscle Hypertrophy
•
Chaillou T et al.
decreased expression of Ehhadh, Hadha, and Hadhb (Table 1)
and of Acadm (about ⫺70% between d3 and d7). Moreover,
Shimizu et al. (28) recently demonstrated that Klf15 activation
was associated with the upregulation of Bcat2, a member of
BCAA transaminase that catalyzes the initial step of degradation for all three of the BCAAs in skeletal muscle. To confirm
our array results, qPCR were performed to assess the mRNA
expression of Klf15, Bcat2, and Ehhdah. Ehhdah was chosen
for validation because it is a common member of enoyl-CoA
hydratase with Hadha and Hadhb. In agreement with the array
findings, the expression of these three genes was decreased in
response to mechanical overload, with the greatest change
observed for Klf15 (Fig. 5). The decreases in mRNA expression were ⬃90%, 42–53%, and 44 –73% between 3 and 7 days
of overload for Klf15, Bact2, and Ehhdah, respectively.
www.targetscan.org) confirmed that these genes are predicted
targets of miR-1. To confirm that the upregulation of these
genes correlates with the downregulation of miR-1, qPCR was
performed. We chose to assess the mRNA expression of Parva
because it is a central regulator of ILK signaling, and Snai2
because it is a transcription factor that regulates the expression
of the ECM protein fibronectin (Fn1) (Fig. 7). As illustrated in
Fig. 7, Parva and Snai2 mRNA were increased in response to
mechanical overload, particularly after d7 (3.46- and 3.91-fold
increase, respectively). In contrast, the expression of miR-1
was markedly reduced from d3 of overload, reaching a 73%
decrease at d7 (Fig. 7). These results show that miR-1 downregulation correlates with the upregulation of two of its predicted targets and suggest that miR-1 may regulate ILK signaling during skeletal muscle hypertrophy.
ILK Pathway
DISCUSSION
control
d3
d5
1.2
mRNA levels
(relative to control muscle)
d7
1.0
0.8
*
0.6
**
0.4
0.2
*
*
***
0.0
KLF15
BCAT2
EHHDAH
Fig. 5. Quantitative PCR of expression of genes involved in the valine
degradation pathway. KLF15 was predicted to be an upstream regulator of
BCAT2 (branched-chain amino acid transaminase 2) and EHHDAH (enoylCoA, hydratase/3-hydroxyacyl CoA dehydrogenase). All results are expressed
as the means ⫾ SE (n ⫽ 6 at each time point). *Significantly different from the
control non-overloaded muscle (d0).
This study was designed to identify putative intracellular
signaling pathways and molecular regulators involved in functional overload-induced skeletal muscle hypertrophy through a
temporal transcriptome analysis. The major findings of the
study are the following: 1) based on gene expression variability
as assessed by PCA and the number of differentially expressed
genes, three (early, intermediate, and late) gene expression
patterns could be distinguished during skeletal muscle hypertrophy; 2) we identified several significant signaling pathways
restricted to each particular gene expression pattern: some
pathways were associated with immune response and injury/
disease (early gene expression pattern), metabolism, mechanotransduction, and mitochondrial function (intermediate gene
expression pattern), and one pathway was related to blood
coagulation (late gene expression pattern); 3) during the intermediate gene expression pattern (d3– d7), many components of
the BCAA degradation pathway were significantly downregulated, in particular the valine degradation pathway, while the
ILK pathway was upregulated; 4) the Klf15 transcription factor
was identified as a putative regulator of the BCAA degradation
pathway, and changes in expression of the muscle-specific
miR-1 are linked to regulation of the ILK signaling.
The PCA results and the number of differentially expressed
genes allowed us to identify three gene expression patterns
during hypertrophy: early (d1), intermediate (d3– d7), and late
(d10 – d14) patterns. IPA also revealed the presence of several
enriched pathways at each specific gene expression pattern
(Fig. 3). Not surprising is the finding that the early response
was characterized by the activation of pathways related to
immune response and disease. This finding probably results
from the infiltration of immune cells into skeletal muscle tissue
in response to muscle ablation and/or muscle damage induced
by overload. We did not focus our attention on these pathways
because their activation is more than likely specific to this
“supra” physiological model of muscle hypertrophy. In this
study, we showed that 1) overload resulted in a progressive
increase in the total RNA content between d3 and d7, which
presumably indicates large increases in ribosomal RNA and
content at a time associated with increased rates of protein
synthesis (20); and 2) the most robust change in gene expression (assessed by the number of differentially regulated genes,
Fig. 2) was observed during this intermediate gene expression
pattern. Therefore, we chose to focus our attention on this
J Appl Physiol • doi:10.1152/japplphysiol.00611.2013 • www.jappl.org
Downloaded from http://jap.physiology.org/ by 10.220.33.4 on June 14, 2017
Another pathway that was found to be enriched during the
intermediate gene expression pattern was the ILK pathway.
The differentially expressed genes related to the ILK signaling
are presented in Table 2. Among the 50 genes that were
differentially expressed at least one time between d3 and d7 of
mechanical overload, 36 (72%) were upregulated, whereas 14
(28%) were downregulated. These genes mainly encoded proteins related to the cytoskeleton and sarcomeric proteins, enzymes, kinases, phosphatases, and transcription regulators. As
shown in Fig. 6, which was derived from our IPA analysis, ILK
is activated upstream by extracellular matrix (ECM) proteins
and growth factors via integrin-␤ complex and receptor tyrosine kinase/insulin receptor substrate/phosphoinositide 3-kinase signaling, respectively, as well as directly by parvin-␣
(27). Once activated, ILK regulates a number of biological
process, including cytoskeleton dynamics, ECM composition,
sarcomere protein expression, and cell proliferation.
Ingenuity Knowledge Base revealed that five of the upregulated genes (Ccnd1, Fn1, Parva, Pik3ca2, and Snai2) were
predicted targets of miR-1, a muscle-specific micro-RNA previously reported to be downregulated in this model of muscle
hypertrophy (19). The online algorithm TargetScan (http://
Transcriptome Analysis During Skeletal Muscle Hypertrophy
•
1071
Chaillou T et al.
Table 2. Differentially regulated genes involved in the integrin-linked kinase signaling
Fold Change
Type(s)
Symbol
Gene Name
Member of/Molecule
d3
d5
d7
IRS1
SH2B2
TMSB10/TMSB4X
Insulin receptor substrate 1
SH2B adaptor protein 2
Thymosin ␤4, X-linked
IRS
IRS
TMSB4
0.50
2.25
6.41
0.77
2.03
9.05
0.96
1.77
8.47
Cyclin
CCND1
Cyclin D1
CCND1
3.99
3.35
3.13
Cytokine
TNF
Tumor necrosis factor
TNF
2.30
1.78
1.34
Cytoskeleton protein
ACTG1
ACTG2
ACTN4
FBLIM1
FLNA
FLNB
MYH9
PARVA
VIM
Actin, ␥1
Actin, ␥2, smooth muscle, enteric
Actinin, ␣4
Filamin binding LIM protein 1
Filamin A, ␣
Filamin B, ␤
Myosin, heavy chain 9, nonmuscle
Parvin, ␣
Vimentin
F-Actin
G-Actin/F-Actin
␣-Actinin
Filanin
Filanin
Filanin
Myosin
PARVA
Vimentin
2.11
0.93
2.03
2.40
2.45
2.50
3.08
2.26
2.15
2.16
2.05
2.14
2.91
2.92
2.94
3.25
2.98
2.16
2.19
2.82
2.32
2.91
3.52
3.17
3.44
3.19
2.20
Enzyme
FNBP1
PTGS2
RHOB
RHOC
RHOU
RND3
Formin binding protein 1
Prostaglandin-endoperoxide synthase 2
Ras homolog family member B
Ras homolog family member C
Ras homolog family member U
Rho family GTPase 3
Ras homolog
PTGS2
Ras homolog
Ras homolog
Ras homolog
Ras homolog
2.02
6.92
1.64
2.90
0.72
1.87
2.20
8.21
1.98
3.05
0.55
2.26
2.43
8.67
2.19
2.72
0.46
2.64
Extracellular matrix protein
FN1
Fibronectin 1
Fibronectin 1
3.10
3.91
4.32
Growth factor
PDGFC
VEGFA
VEGFB
Platelet-derived growth factor C
Vascular endothelial growth factor A
Vascular endothelial growth factor B
VEGF
VEGF
VEGF
2.98
0.57
0.42
4.72
0.39
0.38
5.27
0.28
0.32
Kinase
AKT1
ILK
MAP2K6
MAPK12
PIK3C2A
PIK3R3
RPS6KA5
v-Akt murine thymoma viral oncogene homolog 1
Integrin-linked kinase
Mitogen-activated protein kinase kinase 6
Mitogen-activated protein kinase 12
Phosphoinositide 3-kinase, class 2, ␣-polypeptide
Phosphoinositide-3-kinase, regulatory subunit 3 (␥)
Ribosomal protein S6 kinase, 90 kDa, polypeptide 5
AKT
ILK
MAP2K6
JNK
PI3K
PI3K
MSK1/2
1.90
1.92
0.18
0.60
1.56
0.93
0.42
2.11
2.28
0.24
0.58
1.87
1.82
0.49
2.17
2.53
0.26
0.49
2.23
2.54
0.57
Peptidase
CASP3
Caspase 3, apoptosis-related cysteine peptidase
CASP3
2.42
2.85
2.95
Phosphatase
PPAP2B
PPM1J
PPM1L
PPP2R1B
Phosphatidic acid phosphatase type 2B
Protein phosphatase, Mg2⫹/Mn2⫹ dependent, 1J
Protein phosphatase, Mg2⫹/Mn2⫹ dependent, 1L
Protein phosphatase 2, regulatory subunit A, ␤
PPAP2B
PP2A
PP2A
PP2A
2.35
0.44
0.23
2.38
2.60
0.41
0.24
2.27
2.57
0.33
0.26
2.28
Sarcomeric protein
ACTC1
ACTA2
MYH11
MYH4
MYH7
MYH8
MYL2
MYL3
MYL4
Actin, ␣, cardiac muscle 1
Actin, ␣2, smooth muscle, aorta
Myosin, heavy chain 11, smooth muscle
Myosin, heavy chain 4, skeletal muscle
Myosin, heavy chain 7, cardiac muscle, ␤
Myosin, heavy chain 8, skeletal muscle, perinatal
Myosin, light chain 2, regulatory, cardiac, slow
Myosin, light chain 3, alkali; ventricular, skeletal, slow
Myosin, light chain 4, alkali; atrial, embryonic
G-Actin/F-Actin
G-Actin/F-Actin
Myosin
Myosin
Myosin
Myosin
Myosin
Myosin
Myosin
1.68
1.04
0.38
0.68
0.28
0.93
0.24
0.14
2.31
5.39
2.02
0.43
0.56
0.39
3.63
0.32
0.11
5.79
6.47
2.20
0.49
0.50
0.57
6.09
0.18
0.10
6.39
Transcription regulator
HIF1A
MYC
SNAI1
SNAI2
Hypoxia inducible factor 1, ␣-subunit
v-Myc myelocytomatosis viral oncogene homolog
Snail homolog 1
Snail homolog 2
HIF1A
MYC
SNAI1
SNAI2
3.20
8.43
2.96
2.15
3.55
5.93
2.47
4.36
3.79
4.89
1.92
4.54
Transmembrane receptor
TNFRSF1A
Tumor necrosis factor receptor superfamily, member 1A
TNFR
2.69
2.46
2.21
The nos. represent the fold change of gene expression after d3, d5, and d7 of mechanical overload compared with control non-overloaded muscles. Italicized
numbers mean that the fold change in gene expression is either lower than 50% decrease or lower than 2-fold increase. The selected genes were differentially
expressed (twofold increase or 50% decrease) at least one time between d3 and d7.
intermediate gene expression pattern, which we suggest is
critical for muscle hypertrophy.
It is well-recognized that nutrient signals from BCAAs
contribute to stimulate protein synthesis in skeletal muscle (5).
Here, we showed that several components of the valine degradation pathway are downregulated during the intermediate
gene expression pattern in response to mechanical overload
(Table 1). Interestingly, some of these genes also encode
J Appl Physiol • doi:10.1152/japplphysiol.00611.2013 • www.jappl.org
Downloaded from http://jap.physiology.org/ by 10.220.33.4 on June 14, 2017
Binding protein
1072
Transcriptome Analysis During Skeletal Muscle Hypertrophy
Extracellular space
Cytoplasm
•
Chaillou T et al.
ECM
protein
Growth
factors
Integrin β
RTK
IRS
PARVA
Increased in Ov muscle
PI3K
Increased or decreased
in Ov muscle
ILK
GSK3
Increased in Ov muscle and
predicted target of miR-1
Activates
α-Actinin
SNAI2
CTN β
Inhibits
Actin
Filamin
Actin
Vimentin
FN1
Myosin
ECM
Sarcomere
CCND1
MYC
Myosin
Cytoskeleton
Cell proliferation
Fig. 6. Schematic representation of the ILK signaling and its putative regulation by the micro-RNA-1 (miR-1). This pathway was adapted from the pathway
determined by Ingenuity Pathway Analysis and represents the main genes differentially regulated in response to 3–7 days of mechanical overload. The gene
expression of these components of ILK signaling was determined by microarray and is presented in Table 2. As shown in Table 2, the gene expression of two
members of insulin receptor substrate (IRS) family, IRS1 and SH2B2, is decreased and increased, respectively (represented by green in the figure). The gene
expression of some components of myosin is either increased or decreased (represented by green in the figure; gene expression presented in Table 2). The genes
designated in purple are predicted targets of miR-1. ECM, extracellular matrix; PARVA, parvin-␣; RTK, receptor tyrosine kinase; PI3K, phosphoinositide
3-kinase; GSK3, glycogen synthase kinase 3; FN1, fibronectin 1; SNAI2, snail homolog 2; CTN-␤, ␤-catenin; CCND1, cyclin D1; MYC, v-myc myelocytomatosis viral oncogene homolog.
components essential for the degradation of leucine and isoleucine, the two other BCAAs. Moreover, we showed that
mRNA expression of the transcription factor Klf15 is significantly downregulated during hypertrophy, which correlated
control
d3
d5
5
4
*
1.0
0.8
3
*
0.6
0.4
**
2
*
1
0.2
0.0
mRNA levels
*
d7
1.2
(relative to control muscle)
miR-1 levels
(relative to control muscle)
1.4
0
miR-1
PARVA
SNAI2
Fig. 7. Quantitative PCR of expression of miR-1 and genes involved in the ILK
signaling pathway (PARVA and SNAI2). miR-1 was predicted by targetScan algorithm
to bind PARVA and SNAI2. All results are expressed as means ⫾ SE (n ⫽ 6 at each
time point). *Significantly different from the control non-overloaded muscle (d0).
with the reduced expression of four of its IPA-predicted targets: Ehhadh, Hadha, and Hadhb (3 components of enoyl-CoA
dehydrogenase, a valine and isoleucine-degrading enzymatic
complex), and Acadm (a component of a leucine-degrading
enzymatic complex). In addition to these genes, Bcat2, which
encodes the protein that catalyzes the initial step for the
degradation of all BCAAs, is also downregulated with hypertrophy and has been reported to be regulated by Klf15 in
skeletal muscle (28).
There is accumulating evidence that Klf15 is capable of
influencing muscle mass by regulating the expression of genes
that are part of the BCAA degradation pathway. The inactivation of Klf15 resulted in a modest but significant increase in
lean mass that was associated with a reduced expression of
multiple genes encoding amino acid-degrading enzymes, including Bcat2 (8). However, a more recent study from this
same group reported no change in skeletal muscle mass (9),
while Klf15 null mice developed a severe cardiac hypertrophy
in response to pressure overload (6). Alternatively, Klf15
overexpression was shown to accelerate catabolism of BCAA,
resulting in myotube and skeletal muscle atrophy (28). Elevated levels of Klf15 also suppressed mTOR activity in myotubes (28), a central regulator of protein synthesis shown to be
J Appl Physiol • doi:10.1152/japplphysiol.00611.2013 • www.jappl.org
Downloaded from http://jap.physiology.org/ by 10.220.33.4 on June 14, 2017
Binding
Transcriptome Analysis During Skeletal Muscle Hypertrophy
Chaillou T et al.
1073
suggest that, in addition to mechanical signals and growth
factors, miR-1 may regulate ILK signaling during skeletal
hypertrophy. This finding, in addition to the observation that
miR-1 is downregulated in response to resistance exercise in
young humans (4), suggest that miR-1 could play a central role
in the control of the skeletal muscle mass.
In conclusion, we identified in this study two novel pathways that may be involved in muscle hypertrophy during
mechanical overload. The enzyme-related genes involved in
BCAA degradation (especially valine degradation) are downregulated during the intermediate gene expression pattern of
overload-induced hypertrophy, and this could be partly due to
downregulation of the transcription factor Klf15. This finding
suggests that the downregulation of genes involved in the
degradation of BCAAs could enhance its availability, modulate
mTORC1 signaling, and contribute to the increase in protein
synthesis in response to a hypertrophic stimulus. We also
showed that the ILK signaling pathway is activated during the
intermediate gene expression pattern in response to mechanical
overload. This mechanical stress-associated pathway may have
a central role in cytoskeleton integrity and remodeling in
response to mechanical overload, as well as contribute to the
activation of mTORC1 signaling. Our findings also support the
notion that the downregulation of miR-1 in response to mechanical overload promotes the activation of the ILK pathway.
The identification of Klf15 and miR-1 as two upstream regulators of BCCA degradation and ILK pathway, respectively,
provides targets for future studies investigating the importance
of these pathways in muscle hypertrophy.
ACKNOWLEDGMENTS
We thank Dr. Esther Dupont-Versteegden and Dr. Janna Jackson (Department of Physiology, University of Kentucky) for helpful discussions.
GRANTS
This work was supported by National Institute of Arthritis and Musculoskeletal and Skin Diseases Grant AR-45617 and Merck to K. A. Esser.
DISCLOSURES
No conflicts of interest, financial or otherwise, are declared by the author(s).
AUTHOR CONTRIBUTIONS
Author contributions: T.C., J.D.L., and J.H.E. performed experiments; T.C.
and J.J.M. analyzed data; T.C., K.A.E., and J.J.M. interpreted results of
experiments; T.C. and J.J.M. prepared figures; T.C. and J.J.M. drafted manuscript; T.C., J.D.L., K.A.E., and J.J.M. edited and revised manuscript; T.C.,
J.D.L., J.H.E., K.A.E., and J.J.M. approved final version of manuscript; J.D.L.,
J.H.E., K.A.E., and J.J.M. conception and design of research.
REFERENCES
1. Bodine SC, Stitt TN, Gonzalez M, Kline WO, Stover GL, Bauerlein R,
Zlotchenko E, Scrimgeour A, Lawrence JC, Glass DJ, Yancopoulos
GD. Akt/mTOR pathway is a crucial regulator of skeletal muscle hypertrophy and can prevent muscle atrophy in vivo. Nat Cell Biol 3: 1014 –
1019, 2001.
2. Calvano SE, Xiao W, Richards DR, Felciano RM, Baker HV, Cho RJ,
Chen RO, Brownstein BH, Cobb JP, Tschoeke SK, Miller-Graziano
C, Moldawer LL, Mindrinos MN, Davis RW, Tompkins RG, Lowry
SF. A network-based analysis of systemic inflammation in humans.
Nature 437: 1032–1037, 2005.
3. Deng Y, Meyer SA, Guan X, Escalon BL, Ai J, Wilbanks MS, Welti R,
Garcia-Reyero N, Perkins EJ. Analysis of common and specific mechanisms of liver function affected by nitrotoluene compounds. PLos One 6:
e14662, 2011.
J Appl Physiol • doi:10.1152/japplphysiol.00611.2013 • www.jappl.org
Downloaded from http://jap.physiology.org/ by 10.220.33.4 on June 14, 2017
activated during overload-induced muscle hypertrophy (1, 20,
25). Currently, it is difficult to accurately determine the localization and expression of KLF15 protein in skeletal muscle.
Although the involvement of KLF15 in skeletal muscle hypertrophy remains unclear, our computational analysis suggests
that the decreased expression of Klf15 in response to overload
may contribute to the downregulation of genes that are involved in the degradation of BCAAs. Under such a scenario,
intracellular BCAAs may be elevated and contribute to the
prolonged activation of mTORC1 considered necessary for
muscle hypertrophy. In addition to its role in amino acid
metabolism, Klf15 was recently shown to regulate lipid flux in
skeletal muscle (9); thus, it is not know if these different
functions (regulation of lipid and amino acid metabolism) of
Klf15 are uncoupled during skeletal muscle hypertrophy.
In addition to the BCAA degradation pathway, we also
identified ILK signaling as a robustly regulated pathway during
the intermediate gene expression pattern of hypertrophy. ILK
is a serine/threonine kinase that links integrins with the forcegenerating actin cytoskeleton. As illustrated in Fig. 6, this
kinase coordinates cues from ECM, including distinct signal
inputs from integrins and growth factor receptor tyrosine kinases. This signaling was shown to regulate the cellular structural integrity and homeostasis in skeletal and cardiac muscle
(31, 32). The inactivation of ILK results in the loss of structural
integrity in cardiac muscle (32), while it does lead to the
development of a mild progressive muscular dystrophy, characterized by extensive fibrosis and irregular fiber size with
centralized nuclei in skeletal muscle (31, 32). Although it
remains to be elucidated, the activation of ILK signaling could
contribute to the protection of skeletal muscle from overloadinduced damage, which is clearly observed 7 days following
mechanical overload in this model of hypertrophy (18). Studies
have also provided evidence to suggest that ILK could play a
role in the regulation of the Akt/mTOR signaling in striated
muscle, although these findings remain controversial (15, 31,
32). These findings support the idea that the ILK pathway is
involved in the regulation of muscle remodeling and structural
integrity in response to compensatory overload, as well as
possibly contributes to the increase in protein synthesis resulting from mTORC1 activation.
Our analysis revealed that changes in expression of the
muscle-specific miR-1 may contribute to ILK signaling. MicroRNAs are small noncoding RNAs that repress gene expression
by binding to sequences in the 3=-untranslated region of gene
leading to the inhibition of translation and/or degradation of the
transcript (21). We showed that the marked decrease in miR-1
expression with hypertrophy is associated with increased expression of five predicted target genes that are part of the ILK
signaling pathway. In particular, we confirmed that miR-1
expression was significantly reduced, while the expression of
two upstream activators of ILK, the ILK-binding protein Parva
(27), and Pik3ca2, a subunit of phosphoinositide 3-kinase,
were upregulated, a finding consistent with micro-RNA function. The loss of miR-1 expression may also have contributed
to the increased expression of another target gene within the
ILK pathway, Snai2; Snai2 is a transcription factor that regulates the expression of the ECM-related protein Fn1. As a
confirmed target of miR-1, the upregulation of Ccnd1 expression during hypertrophy is likely, in part, the result of the
decrease in miR-1 expression (33). Altogether, our results
•
1074
Transcriptome Analysis During Skeletal Muscle Hypertrophy
Chaillou T et al.
19. McCarthy JJ, Esser KA. Micro-RNA-1 and micro-RNA-133a expression are decreased during skeletal muscle hypertrophy. J Appl Physiol
102: 306 –313, 2007.
20. Miyazaki M, McCarthy JJ, Fedele MJ, Esser KA. Early activation of
mTORC1 signalling in response to mechanical overload is independent of
phosphoinositide 3-kinase/Akt signalling. J Physiol 589: 1831–1846,
2011.
21. Pasquinelli AE. MicroRNAs and their targets: recognition, regulation and
an emerging reciprocal relationship. Nature reviews Genetics 13: 271–
282, 2012.
22. Pearen MA, Ryall JG, Lynch GS, Muscat GE. Expression profiling of
skeletal muscle following acute and chronic beta2-adrenergic stimulation:
implications for hypertrophy, metabolism and circadian rhythm. BMC
Genomics 10: 448, 2009.
23. Peinnequin A, Mouret C, Birot O, Alonso A, Mathieu J, Clarencon D,
Agay D, Chancerelle Y, Multon E. Rat pro-inflammatory cytokine and
cytokine related mRNA quantification by real-time polymerase chain
reaction using SYBR green. BMC Immunol 5: 3, 2004.
24. Philp A, Hamilton DL, Baar K. Signals mediating skeletal muscle
remodeling by resistance exercise: PI3-kinase independent activation of
mTORC1. J Appl Physiol 110: 561–568, 2011.
25. Sandri M. Signaling in muscle atrophy and hypertrophy. Physiology
(Bethesda) 23: 160 –170, 2008.
26. Sartori R, Milan G, Patron M, Mammucari C, Blaauw B, Abraham R,
Sandri M. Smad2 and 3 transcription factors control muscle mass in
adulthood. Am J Physiol Cell Physiol 296: C1248 –C1257, 2009.
27. Sepulveda JL, Wu C. The parvins. Cell Mol Life Sci 63: 25–35, 2006.
28. Shimizu N, Yoshikawa N, Ito N, Maruyama T, Suzuki Y, Takeda S,
Nakae J, Tagata Y, Nishitani S, Takehana K, Sano M, Fukuda K,
Suematsu M, Morimoto C, Tanaka H. Crosstalk between glucocorticoid
receptor and nutritional sensor mTOR in skeletal muscle. Cell Metab 13:
170 –182, 2011.
29. Spurlock DM, McDaneld TG, McIntyre LM. Changes in skeletal muscle
gene expression following clenbuterol administration. BMC Genomics 7: 320,
2006.
30. von Walden F, Casagrande V, Ostlund Farrants AK, Nader GA.
Mechanical loading induces the expression of a Pol I regulon at the onset
of skeletal muscle hypertrophy. Am J Physiol Cell Physiol 302: C1523–
C1530, 2012.
31. Wang HV, Chang LW, Brixius K, Wickstrom SA, Montanez E,
Thievessen I, Schwander M, Muller U, Bloch W, Mayer U, Fassler R.
Integrin-linked kinase stabilizes myotendinous junctions and protects
muscle from stress-induced damage. J Cell Biol 180: 1037–1049, 2008.
32. White DE, Coutu P, Shi YF, Tardif JC, Nattel S, St Arnaud R, Dedhar
S, Muller WJ. Targeted ablation of ILK from the murine heart results in
dilated cardiomyopathy and spontaneous heart failure. Genes Dev 20:
2355–2360, 2006.
33. Zhang D, Li X, Chen C, Li Y, Zhao L, Jing Y, Liu W, Wang X, Zhang
Y, Xia H, Chang Y, Gao X, Yan J, Ying H. Attenuation of p38-mediated
miR-1/133 expression facilitates myoblast proliferation during the early
stage of muscle regeneration. PLos One 7: e41478, 2012.
34. Zhao C, Dahlman-Wright K. Liver X receptor in cholesterol metabolism. J Endocrinol 204: 233–240, 2010.
35. Zoncu R, Bar-Peled L, Efeyan A, Wang S, Sancak Y, Sabatini DM.
mTORC1 senses lysosomal amino acids through an inside-out mechanism
that requires the vacuolar H(⫹)-ATPase. Science 334: 678 –683, 2011.
J Appl Physiol • doi:10.1152/japplphysiol.00611.2013 • www.jappl.org
Downloaded from http://jap.physiology.org/ by 10.220.33.4 on June 14, 2017
4. Drummond MJ, McCarthy JJ, Fry CS, Esser KA, Rasmussen BB.
Aging differentially affects human skeletal muscle microRNA expression
at rest and after an anabolic stimulus of resistance exercise and essential
amino acids. Am J Physiol Endocrinol Metab 295: E1333–E1340, 2008.
5. Drummond MJ, Rasmussen BB. Leucine-enriched nutrients and the
regulation of mammalian target of rapamycin signalling and human
skeletal muscle protein synthesis. Curr Opin Clin Nutr Metab Care 11:
222–226, 2008.
6. Fisch S, Gray S, Heymans S, Haldar SM, Wang B, Pfister O, Cui L,
Kumar A, Lin Z, Sen-Banerjee S, Das H, Petersen CA, Mende U,
Burleigh BA, Zhu Y, Pinto YM, Liao R, Jain MK. Kruppel-like factor
15 is a regulator of cardiomyocyte hypertrophy. Proc Natl Acad Sci U S
A 104: 7074 –7079, 2007.
7. Glass DJ. Skeletal muscle hypertrophy and atrophy signaling pathways.
Int J Biochem Cell Biol 37: 1974 –1984, 2005.
8. Gray S, Wang B, Orihuela Y, Hong EG, Fisch S, Haldar S, Cline GW,
Kim JK, Peroni OD, Kahn BB, Jain MK. Regulation of gluconeogenesis by Kruppel-like factor 15. Cell Metab 5: 305–312, 2007.
9. Haldar SM, Jeyaraj D, Anand P, Zhu H, Lu Y, Prosdocimo DA,
Eapen B, Kawanami D, Okutsu M, Brotto L, Fujioka H, Kerner J,
Rosca MG, McGuinness OP, Snow RJ, Russell AP, Gerber AN, Bai X,
Yan Z, Nosek TM, Brotto M, Hoppel CL, Jain MK. Kruppel-like factor
15 regulates skeletal muscle lipid flux and exercise adaptation. Proc Natl
Acad Sci U S A 109: 6739 –6744, 2012.
10. Hao C, Xie Y, Peng M, Ma L, Zhou Y, Zhang Y, Kang W, Wang J, Bai
X, Wang P, Jia Z. Inhibition of connective tissue growth factor suppresses hepatic stellate cell activation in vitro and prevents liver fibrosis in
vivo. Clin Exp Med. In press.
11. Heymann F, Hammerich L, Storch D, Bartneck M, Huss S, Russeler
V, Gassler N, Lira SA, Luedde T, Trautwein C, Tacke F. Hepatic
macrophage migration and differentiation critical for liver fibrosis is
mediated by the chemokine receptor C-C motif chemokine receptor 8 in
mice. Hepatology 55: 898 –909, 2012.
12. Kendziorski C, Irizarry RA, Chen KS, Haag JD, Gould MN. On the
utility of pooling biological samples in microarray experiments. Proc Natl
Acad Sci U S A 102: 4252–4257, 2005.
13. Koh TJ, Pizza FX. Do inflammatory cells influence skeletal muscle
hypertrophy? Front Biosci 1: 60 –71, 2009.
14. Lewis BP, Burge CB, Bartel DP. Conserved seed pairing, often flanked
by adenosines, indicates that thousands of human genes are microRNA
targets. Cell 120: 15–20, 2005.
15. Lu H, Fedak PW, Dai X, Du C, Zhou YQ, Henkelman M, Mongroo
PS, Lau A, Yamabi H, Hinek A, Husain M, Hannigan G, Coles JG.
Integrin-linked kinase expression is elevated in human cardiac hypertrophy and induces hypertrophy in transgenic mice. Circulation 114: 2271–
2279, 2006.
16. MacNeil LG, Melov S, Hubbard AE, Baker SK, Tarnopolsky MA.
Eccentric exercise activates novel transcriptional regulation of hypertrophic signaling pathways not affected by hormone changes. PLos One 5:
e10695, 2010.
17. Mahoney DJ, Safdar A, Parise G, Melov S, Fu M, MacNeil L, Kaczor
J, Payne ET, Tarnopolsky MA. Gene expression profiling in human
skeletal muscle during recovery from eccentric exercise. Am J Physiol
Regul Integr Comp Physiol 294: R1901–R1910, 2008.
18. Marino JS, Tausch BJ, Dearth CL, Manacci MV, McLoughlin TJ,
Rakyta SJ, Linsenmayer MP, Pizza FX. Beta2-integrins contribute to
skeletal muscle hypertrophy in mice. Am J Physiol Cell Physiol 295:
C1026 –C1036, 2008.
•