Using molecular biology to understand muscle growth

Abstract

Skeletal muscle has a tremendous capacity to adapt to the functional and metabolic loads placed upon it. This plasticity is most apparent in the physique of the strength athlete who has utilized the principles of progressive resistance exercise to improve their skeletal muscle mass and function. The most striking adaptation to progressive resistance exercise is muscle hypertrophy: the postnatal growth of muscle fibres. Since the pioneering work of Goldberg in the 1960s, a great deal of research has been carried out to understand the mechanisms governing this adaptation. Two cellular responses are

Full text

Research Signpost
37/661 (2), Fort P.O., Trivandrum-695 023, Kerala, India
Muscle Plasticity – Advances in Biochemical and Physiological Research, 2009: 45-93
ISBN: 978-81-308-0322-7 Editors: José Magalhães and António Ascensão
2
Molecular mechanisms of
skeletal muscle hypertrophy
Using molecular biology to
understand muscle growth
D. Lee Hamilton, Matthew G. MacKenzie and Keith R. Baar
Functional Molecular Biology Lab, Department of Molecular Physiology
University of Dundee, UK
Abstract
Skeletal muscle has a tremendous capacity to
adapt to the functional and metabolic loads placed
upon it. This plasticity is most apparent in the
physique of the strength athlete who has utilized the
principles of progressive resistance exercise to
improve their skeletal muscle mass and function. The
most striking adaptation to progressive resistance
exercise is muscle hypertrophy: the postnatal growth
of muscle fibres. Since the pioneering work of
Goldberg in the 1960s, a great deal of research has
been carried out to understand the mechanisms
governing this adaptation. Two cellular responses are
Correspondence/Reprint request: Dr. Keith Baar, Functional Molecular Biology Lab, Department of Molecular
Physiology, University of Dundee, UK. E-mail: FMBlab@googlemail.com

D. Lee Hamilton et al.
46
required for hypertrophy: increased protein synthesis and an altered
transcriptional profile. The control of protein synthesis is regulated by the
mammalian target of rapamycin complex 1 (mTORC1), while the changes in
mRNA levels could be due to β-catenin and/or micro-RNAs. mTORC1 is a
kinase that controls the rate and capacity for translation, β-catenin is a
regulator of transcription, and microRNAs act post-transcriptionally to
suppress expression of families of mRNA that control muscle size, cellular
differentiation and growth. The aim of this chapter is to discuss the
regulation of these and other key molecules by resistance exercise and to
highlight their respective roles in skeletal muscle hypertrophy. A secondary
aim is to distinguish between muscle growth per se and resistance exercise-
induced muscle hypertrophy highlighting the fact that a molecule may be
involved in postnatal muscle growth without affecting hypertrophy.
1. Introduction
The term “Progressive Resistance Exercise” was first developed by two
physicians, DeLorme and Watkins, shortly after World War II [1]. The term
was used to describe a method of exercise that improved the muscle function
of recovering Polio patients. The patients started off lifting a manageable
weight. During the training period, the weight was increased progressively so
that after some time their manageable weight was heavier than at the beginning.
This example of training follows the overload principle – the stimulus is of
sufficient frequency, intensity and duration to illicit an adaptation. The
adaptation in this example is improved muscle strength and mass. The overload
principle in the form of progressive resistance exercise has been exploited by
athletes for millennia as a means to increase their muscle mass and strength to
gain a competitive edge. Recently however, other populations have become
interested in increasing muscle mass and strength. Due to improved longevity
[2], health care systems are facing a burden in the care of frail elderly [3,4].
Furthermore, a number of disease states result in frailty including: HIV/AIDS
[5], Cancer [6], Sepsis [7], COPD [8], and Diabetes [9]. This frailty is largely
due to the loss of muscle mass [10,11]. Treatments that prevent muscle
wasting or stimulate muscle growth would relieve much of this burden and
improve the quality of countless lives. For this reason the molecular
mechanisms of resistance exercise-induced skeletal muscle hypertrophy have
been intensively studied for the past 40 years.
1.1 Models of resistance exercise and hypertrophy
Human resistance exercise
A typical program of human resistance training results in an average
muscle enlargement of 0.1% per day. These programs typically consist of 1-5

Molecular mechanisms of skeletal muscle hypertrophy
47
sets of 6-10 different exercises that target specific muscle groups. Each set
consists of 6-8 repetitions with loads between 67 and 75% of maximal
voluntary force. A typical one set program will take 20 minutes to complete,
while a 3 set program will take approximately 1 hour. During this time, muscle
is performing active work for between 8 and 24 minutes and this is done 2-3
days a week [12]. This means that work-induced muscle hypertrophy results
from as little as 20 minutes of high intensity muscle contraction a week.
A single bout of this type of resistance exercise in the fasted state can
increase both the rate of protein degradation [13,14] and synthesis for
36-48hrs [13-16]. With proper nutrition, synthesis can be further increased
while degradation is decreased resulting in a net increase in protein balance
[17-20]. After repeated bouts of resistance exercise, the intermittent changes
in protein turnover accumulate resulting in a new steady state with a greater
number of myofibrils in parallel [21], increasing the radiological density [22]
and radial diameter [21-23] of the muscle and improving the capacity to
produce force [21-23]. However, studying molecular responses to exercise in
humans is limited by the variability in responsiveness to exercise [24], the
small amounts of tissue that can be collected in such studies, and difficulties
in recruiting and maintaining subjects on a study for a prolonged period. As
skeletal muscle hypertrophy is evolutionarily conserved across many species,
several animal and tissue culture models have been developed to aide in the
study of hypertrophy.
Animal studies
Since the seminal work of Goldberg in the 1960’s [25], models of human
resistance exercise have been developed for animals as diverse as quails, mice,
rats, cats and horses. These models have been extremely useful for describing
the mechanisms underlying muscle hypertrophy. Animal models have five
primary advantages over human studies: (1) inbred strains of animals reduce
genetic variation; (2) nutrition and environmental factors can be tightly
regulated; (3) drugs and radioactive tracers can be used; (4) whole muscles
can be removed for a variety of functional, biochemical, histochemical, and
genetic analyses; and most importantly, (5) genetic engineering can be utilised to
assess the contribution of specific genes to the hypertrophic process. There is
little doubt that the introduction of animal models to study hypertrophy has
greatly advanced our molecular understanding of skeletal muscle growth.
However, the animal data must be interpreted with caution; keeping the
physiological relevance of the model in mind. When considering the
physiological relevance of an animal model we must take into account several
questions: (1) how closely does the intervention replicate human progressive
resistance exercise? (2) what is the time course of the growth and is this

D. Lee Hamilton et al.
48
comparable to the time course in humans relative to the life span? and (3) are
the biochemical adaptations within the muscle comparable to human
progressive resistance exercise? When using these criteria, animal models can
be largely separated into two categories: those that are physiological and
those that are non-physiological. We will give only a brief description of the
animal models that are currently used to study the molecular control of
muscle hypertrophy and compare these with human muscle hypertrophy. For
more in depth analysis of this area see reviews by Booth & Thomason [12],
Timson [26], and Lowe & Alway [27].
Non-physiological models of hypertrophy
The first rodent muscle hypertrophy models developed in the 1940’s
were based on the finding that passive stretch is sufficient to induce growth.
For instance, immobilising the ankle joint with the soleus in a stretched position
[28] or denervating one half of the diaphragm [29] resulted in hypertrophy that
was followed by atrophy of the immobilised/denervated muscle. As these
models were ineffective at maintaining hypertrophy, other stretch models
have since replaced them. Avian stretch models, whereby weights are
attached to the wing of an adult chicken have been used to induce an 80%
hypertrophy (measured by wet weight) in the first week followed by a further
doubling of muscle size by 5 weeks in the anterior latissimus dorsi (ALD)
muscle [30]. This model has been widely used to study the genetic response
to a hypertrophic stimulus [31]. Although these models are effective at
inducing and maintaining hypertrophy, they do not replicate human hypertrophy,
the time-course is much faster, and some of the phenotypic changes, specifically
the hyperplastic response, are unlike work-induced skeletal muscle hypertrophy.
A modified stretch model that also creates a load-induced hypertrophy is
compensatory overload. This model involves severing the tendon (tenotomy),
nerve (synergist denervation) or completely removing the synergist (synergist
ablation) in a group of muscles, most commonly the gastrocnemius in
rodents. The remaining muscles compensate for the loss of the synergist(s) by
increasing their functional capacity. Tenotomy was developed in the 1960’s
and induces a rapid increase in the wet weight of the soleus and plantaris of
30-50% and 20% respectively within 6 days [32]. However, simple tenotomy
permits the reattachment of the severed tendon increasing the variability of
the model. To overcome this shortcoming, in the 1970’s the synergist
ablation model was developed. Removal of the tibialis anterior results in 15%
hypertrophy of the remaining extensor digitorim longus within 4 days [33]
and removal of the gastrocnemius results in a 103% and 45% increase in fibre
area of the plantaris and soleus respectively after 60 days [34]. Due to the
relative ease of the surgery and low contact time with the animals, synergist

Molecular mechanisms of skeletal muscle hypertrophy
49
ablation is the most widely used long-term model of skeletal muscle hypertrophy.
However, the load is present whenever the animal is active (up to 12 hours a day)
and this does not replicate human resistance exercise where the load is present for
a few minutes a week. Typically, programs of human resistance exercise result in
increased muscle volume, as measured by MRI, of 9.1-14% over periods of 9-14
weeks [35]. Compare this to the massive increases in mass seen over a matter of
days with overload and clearly the overload models provide a much greater
hypertrophic response than is possible in human work-induced hypertrophy.
Lastly, compensatory hypertrophy models result in a shift in myosin isoform
towards slow twitch fibres [36], whereas long-term resistance training in humans
does not result in major changes in fibre type composition [37,38], even though a
shift from Type IIB to Type IIA can be seen [39,40]. Therefore, even though
overload is effective and broadly used, it is not a physiological model of muscle
hypertrophy. For this reason, data from this model must be interpreted with a
degree of caution.
Physiological models of skeletal muscle hypertrophy
As a result of concerns with the synergist ablation model, several groups
have developed animal models of progressive resistance exercise that more
closely mimic the loading patterns, growth response and biochemical
adaptations seen in humans. Animal models designed to mimic human
resistance exercise include operant conditioning and in vivo electrical
stimulation. The first operant conditioning models were developed by Goneya
& Ericson in 1976 [41]. These authors conditioned adult cats to flex their right
paw against a footplate for a food reward. Once conditioned, the animals were
exercised 5 days a week for up to 41 weeks and experienced an increase in wet
weight of the flexors of 7-34%. The successful feline model was followed by
several rodent variations in the 1980’s, whereby rats were conditioned to
perform squat type exercises for a food reward [42]. One such model developed
by Ho et al. [43] involved conditioning rats to respond to a light stimulus and
reach for a steel bar with weights attached to their abdomen, motivation came
in the form of an electric shock. The rats were exercised for 4 days a week for 8
weeks. The program consisted of performing 16 squat like movements that
took 2 secs to complete with a 30 sec rest between each squat. The weight was
progressively increased until the rats were lifting 130% of their body weight
resulting in a 12% increase in the mass of the adductor longus. These models
are effective at inducing a degree of hypertrophy over a time course that more
closely resembles the human response. However, they are very demanding on
the researchers time and lack an inter-animal control.
The next generation of animal models came with the development of
electrical stimulation in anaesthetised animals. Wong & Booth [44] developed

D. Lee Hamilton et al.
50
the first of these models in 1988 when they anaesthetised animals and used
platinum electrodes to stimulate the muscles of the right leg. A footplate
connected to a weighted pulley was strapped to the foot and upon stimulation
the foot plantar flexed lifting a weight that was progressively increased over
the training period. Various paradigms were utilized, with the paradigm of 4
sets of 6 repetitions with 3 days of recovery resulting in a doubling of the
weight lifted and a 13-18% hypertrophy of the plantar flexors over 16 weeks
of training. There was however, some concern over possible damage caused
by applying an electrical current directly to the muscle. Consequently, Wong
and Booth’s model was modified so that the platinum wire electrodes were
implanted on the right sciatic nerve instead. As all the muscles below the
point of stimulation contract maximally, the foot plantar flexes due to the
greater mass in the posterior compartment of the leg. As a result the muscles
in the anterior compartment (tibialis anterior and extensor digitorim longus)
contract eccentrically. The protocol consisted of 10 sets of 6 repetitions twice
a week for 6 weeks. The end result was approximately 14% hypertrophy of
the TA and EDL [80]. While both the electrical stimulation and operant
conditioning models are very effective, the stimulation models require much
less time and provide a greater degree of control over the exercise paradigm. In
electrical stimulation, the investigator controls the number of repetitions and
the rest periods during the program and the completion of the exercise bout is
distinct so that effective measures of the dynamics after exercise can be made.
In vitro models
In order to gain even greater levels of control over experimental conditions,
researchers have turned to ex vivo models of resistance exercise and tissue
culture. Ex vivo whole muscle models were first used to study the response to
hypertrophic stimuli in the 1970’s (reviewed by Vandenberg, [45]). They
presented several advantages over whole animal systems. The primary
advantage was the control over the external milieu. The researcher could add or
remove anything to the media and assess the effect on the exercise response.
Also, a variety of stimulation patterns could be rapidly assessed using
automatic servo motors set up to carry out various protocols. The major
disadvantage of these models has been the inability to preserve whole muscles
in culture for long periods due to difficulties in maintaining the nitrogen
balance and cell death due to hypoxia [45]. Thus, ex vivo muscle models are
only useful for studying acute effects and there are questions regarding whether
the stretch or the hypoxic conditions lead to the changes measured.
Due to the problems associated with whole muscle ex vivo models,
researchers have turned to tissue culture models. Cell or tissue culture techniques
have been around since the late 1800’s, however it took almost a further 150

Molecular mechanisms of skeletal muscle hypertrophy
51
years before tissue culture became widely used. Interestingly, like the
development of progressive resistance exercise, the wide-scale use of tissue
culture can be traced to Polio. In 1949, the discovery that cultures of animal
cells could be used to generate the Poliovirus resulted in the development of
the techniques required to generate large quantities of Poliovirus for vaccine
production. Over time, a greater variety of cells were cultured with
immortalised muscle cells produced in 1977 [46]. Vandenberg & Kauffman,
[47] developed the first in vitro tissue culture model of skeletal muscle
hypertrophy. They grew chicken embryonic myoblasts on a stretching frame
until they differentiated into myotubes and then applied a stretch of 5-20%
for 18 hours. These stretch protocols resulted in a 6-9.5% increase in total
protein content. However, like the ex vivo muscle explant models, tissue
culture systems lack systemic interactions. But a further limitation to tissue
culture is that most of these experiments are preformed in 2-dimensions
making it very difficult to measure the true functional changes that occurs
within muscle. This particular disadvantage is gradually being overcome
through the development of tissue engineering techniques that can
consistently produce homogenous 3-dimensional muscle in culture, but these
models are not widely in use at this time.
Obviously, the gold standard model for studying the molecular
mechanism of skeletal muscle hypertrophy is human studies. However, due
to the limitations of such studies other models are required. No single model
is appropriate for all situations. However, it is important to use a range of
different models and be cautious when interpreting data from non-physiological
models of muscle hypertrophy. When determining the requirement of a specific
gene for the molecular response to resistance exercise, overload in transgenic
animals has been used exclusively. While this can give some general insight,
it is then necessary to go back and use a more physiological model to determine
what stage of the growth response is affected by the gene of interest. It is only
in this way that we will completely understand how increased load is transduced
to a signal that increases protein synthesis and alters gene expression.
1.2 Signalling to growth
The first major question in the field of skeletal muscle hypertrophy is;
what is it about resistance exercise that is transduced into growth? Although
there is no definitive answer to this question at the moment, there are clues as
to what the muscle is sensing. First, stretch of the soleus by immobilising the
ankle joint in a dorsi flexed position [28]; weight [30], or spring loaded
devices [48] applied to chicken wings; and stretch in cultured muscle cells all
induce hypertrophy [47]. Second, denervating the stretched muscle in the rat
[49] or the chicken [30] does not prevent hypertrophy, whereas denervation

D. Lee Hamilton et al.
52
of the antagonistic muscles, that provide the stretch, does prevent growth
[33]. Third, hypertrophy only occurs when sufficient weight is applied to the
muscle [50,51]. Finally, fourth, hypertrophy can occur in the absence of
external nutrients, pituitary or thyroid hormones or insulin [25]. Together,
these early animal studies suggest that tension, either passive or active force
across a muscle, is the signal that leads to muscle hypertrophy and points to a
force sensor (mechanosensor) within the muscle that can transduce tension
into a growth signal. However, even though some of the downstream signals
have been identified, the identity of this force sensor has been allusive.
Integrin-associated signalling
The ideal position for a mechanosensor is within the region connecting
the internal architecture of the cell to the extracellular matrix or to other cells
[52]. A number of protein complexes serve this purpose in muscle including
the dystrophin-associated glycoprotein complex, integrin associated
costameric complex, and cell-cell adherins junctions. The role of some of
these protein complexes in mechanosensing has been studied extensively in
the heart. In the heart, proteins that are associated with costameres appear to
be important in sensing mechanical stretch and initiating the hypertrophic
response. Specifically, -integrins [53,54], the integrin-linked kinase (ILK)
[55], melusin [56,57], the muscle LIM domain protein [58], and protein
kinase B/akt all are found in costameres and all play an important role in
mechanosensing in the heart. Of specific interest are ILK and PKB/akt. ILK
is both a kinase and a scaffolding protein. It interacts with -integrins,
PKB/akt, and the rapamycin-insensitive companion of mTOR (rictor). As
will be discussed later, rictor activates PKB/akt and the activation of PKB/akt
can lead to muscle hypertrophy in both the heart and skeletal muscle [59].
This suggests that ILK may coordinate the conversion of the mechanical
stimulus from the -integrins into the chemical signal of PKB
phosphorylation.
Another member of costameres and adherins junctions is focal adhesion
kinase (FAK) [60]. Work by Fluck et al. [61] demonstrated an increase in the
amount and activity of FAK within 24-36 hours of the onset of stretch in the
chicken ALD and in the rat soleus after overload. Furthermore, the degree of
FAK expression and activity in muscles is related to their loading [62]. For
instance, the postural soleus muscle has greater expression and tyrosine
phosphorylation of FAK than the gastrocnemius or plantaris [62]. Moreover,
unloading reduces the phosphorylation of FAK in the soleus and the
concentration of FAK in the gastrocnemius and plantaris [62]. This suggests
that load may be sensed through FAK. However, unlike ILK, it is unclear
what the downstream targets of FAK may be involved in this process.

Molecular mechanisms of skeletal muscle hypertrophy
53
Calcium signalling
Calcium has also been proposed as a mechanosensor mediating hypertrophic
responses. Guharay and Sachs [63] first identified stretch-activated ion channels
in skeletal muscle in 1984. These channels respond to membrane deformation
with increased open time allowing calcium influx that can be blocked by the
antibiotic streptomycin and the cation gadolinium. Treating animals with
either streptomycin or gadolinium prior to a bout of resistance exercise can
decrease the signalling associated with muscle growth [64], suggesting that
these channels may be important in mechanosensing. Further support for this
theory comes from the fact that increasing intracellular calcium in muscle
cells using the calcium ionophore A23187 enhances both protein synthesis
and degradation rates [65] much like resistance exercise, and the calcineurin
inhibitors, cyclosporin A and FK506, prevent growth factor-induced muscle
hypertrophy in culture [66]. However, calcineurin is not activated by
overload hypertrophy, pharmacological blockade with cyclosporin A does not
prevent overload-induced muscle growth [59], and mice lacking calcineurin
can still undergo overload-induced hypertrophy [67]. Futhermore, stretch in
combination with A23187 enhanced protein synthesis further than treatment
with A23187 alone [65]. Together, these data suggest that although calcium
influx may play a role in mechanosensing, it is not the only sensor and it does
not signal though calcineurin.
Growth factors
Growth factors released in an autocrine manner in response to stretch
have also been proposed as a mechanosensor. Growth factors such as the
IGFs (insulin like growth factors) have important roles in myogenesis and
muscle growth through their ability to regulate proliferation, differentiation
and growth hypertrophy [68]. Turner et al. [69] demonstrated that implanting
growth hormone secreting GH3 cells into rats induced an increase in the
expression of IGF-I (8-fold) and IGF-II (6-fold) and resulted in skeletal
muscle hypertrophy. DeVol et al. [66] found that tenotomy of the
gastrocnemius increased the mRNA expression of IGF-I and IGF-II in
normal and hyposectamized animals, demonstrating that load could increase
the expression of the IGFs independent of growth hormone. Stretch in rabbits
also induces the expression of a splice variant of IGF-1 called mechano-
growth factor (MGF) that may be invovled in muscle hypertrophy [70]. The
role of growth factors in the response to hypertrophic stimuli has recently
been brought into question by experiments using genetically modified mice
overexpressing a dominant negative form of the IGF receptor [71]. These
mice underwent normal overload hypertrophy despite being growth factor
resistant, suggesting that while IGF-1 plays an important role in developmental

D. Lee Hamilton et al.
54
growth [71,72] IGFs may not play a role in resistance exercise-induced
hypertrophy.
Other growth factors including fibroblast growth factor-2 (FGF2) are
also released in response to mechanical stimulation. Using cultured human
myotubes grown on stretching apparatus, Clarke and Feeback, [73]
demonstrated that passive stretch induces the release of FGF2 into the culture
media in a stretch dependant manner. Furthermore, antibodies directed against
FGF2 inhibited the stretch-induced growth of the myotubes [73]. More recently
Baar et al. [74], showed that conditioned media from stretched myotubes is
sufficient to activate growth signalling when applied to unstretched myotubes.
However, this was not confirmed in muscles mechanically stimulated ex vivo
[88] suggesting that the data from Baar et al. [74] may be an artifact of 2D
culture. Taken together, it is unlikely that autocrine/paracrine release of growth
factors serves as the mechanosensor in muscle tissue.
Amino acids
Vandenburgh and Kaufman [75] observed that when muscle cells were
stretched there was an increase in the uptake of amino acids. This increased
amino acid uptake could be completely blocked by the sodium channel
inhibitor oubain. Further, ouabain could completely block the stretch-induced
activation of protein synthesis [76]. This work has been supported by studies
showing that amino acid transport in the soleus and plantaris is increased
following synergist tenotomy in normal and hyposectamized rats [77] and
3 hrs following resistance exercise in humans [14]. Together, these data
suggest that stretch activation of amino acid influx may function as a
mechanosensor in skeletal muscle. If intracellular amino acids are a
mechanosensor, then increasing intracellular amino acid content by protein
feeding or stimulating protein degradation within the muscle should increase
the response to resistance exercise. This has been confirmed in a number of
studies showing that protein supplementation in association with resistance
exercise has a synergistic effect on protein synthesis rates [17,19,20] and as
discussed above a significant correlation exists between the fractional
synthetic rate (FSR) and fractional breakdown rate (FBR) within a muscle
after a single bout of resistance exercise in humans. Thus, muscle could
transduce mechanical stimulation into accelerated protein synthesis by
increasing internal amino acid concentration through amino acid transport
and activation of proteolysis.
It is unlikely that one single factor is responsible for growth signalling in
response to resistance exercise. Under different loading parameters it is
possible that a different mechanical sensor is used. Alternatively, one of the
mechanosensory pathways may lead to the activation of the others. At the

Molecular mechanisms of skeletal muscle hypertrophy
55
moment, the costamere appears to be the most likely mechanosensor,
however, it will be important to determine whether the costamere phenotypes
observed in cardiac muscle are conserved in skeletal muscle.
Like the mechanosensor, the molecular mechanisms governing the growth
processes are still controversial. It is clear however, that two key cellular
responses are required for hypertrophy: increased protein synthesis [78] and an
altered transcriptional profile [77]. Here, we will focus on the kinase mTORC1
and its potential role in regulating protein synthesis and capacity and β-Catenin
and micro (mi)RNA for their roles in controlling the levels of families of
mRNA within the cell in response to work-induced hypertrophy.
2. Translational control and mTORC1
An increased rate of translation in response to resistance exercise has
been observed in humans, rats, chicken, and rabbits. The acceleration of
protein synthesis is due to an increase in mRNA activity, the amount of
protein produced from a molecule of mRNA. This was first observed by
Wong & Booth [79] in rats using electrical stimulation. They demonstrated
that up to 48hrs following a single bout of resistance exercise the increase in
protein synthesis as measured by incorporation of L-[4,5-3H]leucine was
predominantly due to an increased protein translation rate rather than an
increase in mRNA. This has since been demonstrated in humans [15] where
muscle protein synthesis and mRNA activity are increased 4-24hrs after
resistance exercise performed by the biceps brachii. Consistent with these
findings, resistance exercise in rats causes a large shift in the polysome
profile such that more ribosomes bind to the available mRNA, which
indicates enhanced translation initiation [80].
Translational control refers to the control of gene expression through the
regulated translation of specific mRNAs to protein. The rate of translation
refers to the overall amount of protein produced from the cytoplasmic pool of
mRNA. Therefore, when referring to increased translation, this indicates an
increase overall protein produced, whereas translational control refers more
specifically to increase synthesis from only specific transcripts. Translation is
an energy costly process (four high energy phosphates per peptide bond [81])
and as such the rate of protein synthesis must be tightly controlled. The
energy status of the cell, endocrine milieu, mRNA content, and the
concentration and specific activities of the translational machinery are some
factors that control translation either specifically or more generally.
One protein that plays an important role in translation and translational
control is mTORC1. mTORC1 coordinates inputs from mechanical stimuli [82],
energy status [83] and from endocrine [84] and nutrient [85] signals to regulate

D. Lee Hamilton et al.
56
the rate of and capacity for protein translation [86]. mTORC1 regulates translation
generally as well as exerting translational control of specific, suppressed
mRNA. How mTORC1 is thought to control these processes as well as how
each might contribute to accelerated protein synthesis will be discussed below.
Overload hypertrophy is dependent on increased mTORC1 activity [59]
and ex vivo models have demonstrated an increase in mTORC1 activity
following passive stretch [74,87,88]. Several animal models of resistance exercise
have confirmed an increase in mTORC1 activity anywhere from 10mins [89]
(rats operantly conditioned to squat) to 6 hrs (stimulation of the sciatic nerve)
[80,90,91] into the recovery period. Several different modes of resistance exercise
in humans have also been shown to activate mTORC1 with a similar time course
[92,93]. Furthermore, in both rats and humans the activation of mTORC1
correlates with muscle hypertrophy and in humans the increase in strength
following training (Figure 1; [94]). Due to the central role of mTORC1 in protein
translation and regulation by resistance exercise and mechanical stimuli,
mTORC1 is thought to be one of the central regulators of hypertrophy.
Figure 1. Correlation between S6K1 phosphorylation 30 minutes after resistance
exercise and the increase in: (A) strength; (B) whole body and (C) leg fat free mass;
and (D) type II fibre hypertrophy (from [94]).

Molecular mechanisms of skeletal muscle hypertrophy
57
2.1 Overview of translation
Translation of protein results from the integration of three distinct
processes: initiation; elongation and termination. Initiation is the assembly of
the components of the translational machinery. Elongation is the process by
which amino acid residues are added to the C-terminal end of the growing
polypeptide chain. Termination is the process by which the peptidyl-tRNA is
cleaved releasing the protein. Under normal conditions, ribosomes are stacked
80-100 nucleotides apart along an mRNA. However, they are capable of
stacking much closer, up to 27-29 nucleotides apart [95], allowing a 3 fold increase
in RNA activity. For this reason it is the initiation phase (in most circumstances)
which acts as the rate-limiting step in translation [96]. We will give a brief
description of the translation process, for more information on this topic see the
following excellent reviews; Kozak, [97], Hershey, [98] and Gingras et al. [96].
The first step of translation initiation is the formation of the 43S
preinitiation complex. Central to this process is eIF2. When bound to GTP,
eIF2 primes the 43S ribosomal subunit for mRNA binding. eIF2 is therefore
critical for the translation of all mRNA. The second step is the association of
mRNA with the 43S ribosomal subunit. This step is catalysed by eIF4E binding
to the 5' cap of the mRNA [96] and its association with eIF4G within the 43S
complex. The association of eIF4E with eIF4G, and thus the mRNA with the
43S ribosomal subunit, is regulated by a competitive inihibitor, the eIF4E
binding protein (4EBP). When hypophosphorylated, 4EBP binds to eIF4E and
prevents the association with eIF4G and translation of the mRNA.
Phosphorylation of 4EBP relieves this inhibition and permits initiation. The
third step of initiation is the melting of the secondary structure of the 5' end of
the mRNA, scanning for the initiation codon, and formation of the 80S
ribosome. Unwinding and scanning of the mRNA is catalysed by eIF4A and
eIF4B and is essential for the translation of mRNA with large amounts of
secondary structure in their 5' untranslated regions such as growth factors and
protooncogenes. Binding of the 60S is promoted by the hydrolysis of eIF2 GTP
to GDP and the release of eIF2 from the preinitiation complex. This results in
the formation of the 80S ribosome and the onset of protein synthesis.
2.2 mTORC1 and cell size regulation
Many of the processes detailed above are regulated by the activity of
mTOR. mTOR is a member of the phosphatidylinositol kinase (PIK) related
kinases [99], although it does not possess lipid kinase activity and is instead
an efficient serine/threonine protein kinase [100]. The activity of mTOR is
dependent upon several adapter proteins GβL [101], raptor [102], rictor
[103], Sin1 [104] and Proctor/PRR5 [105,106], which form two separate

D. Lee Hamilton et al.
58
mTOR complexes capable of regulating distinctive pathways. The mTOR
complex 1 contains GβL, raptor and mTOR and is rapamycin sensitive. GβL
functions to stabilise the association between mTORC1 and raptor and
enhances the kinase activity of mTORC1 towards its targets [117], however it
is not essential for mTORC1 activity [107]. Raptor is an adapter protein that
identifies and binds substrates that contain TOS (TOR signalling) motifs [108]
such as 4EBP and S6K1 [109]. mTORC2 on the other hand consists of mTOR,
GβL, Sin1 and Proctor/PRR5 and is rapamycin insensitive [103]. G L again is
essential for the association between rictor and mTOR, which is essential for
the kinase activity of the complex [104,107]. Sin1 is also required for this
association and kinase activity [104]. The role of Proctor/PRR5 appears less
clear. While its expression is regulated by rictor, it associates with the
mTORC2 complex, and it participates in growth factor signalling down stream
of mTORC2 [106], it is not essential for mTORC2 complex formation or
function [105]. mTORC2 regulates distinct targets from mTORC1, for instance
mTORC2 is proposed to act upstream of the Rho GTPases [110], modulates the
phosphorylation of PKC [103] and PKB [111], and has a role in regulating the
cellular cytoskeleton [103,110]. As the regulation of mTORC1 by resistance
exercise and overload is better characterised than mTORC2 we will focus on
mTORC1 in the regulation of cell size and protein synthesis.
Much of our knowledge about the role of mTORC1 in the regulation of
translation and cell size has come from genetic studies and the use of
rapamycin in drosophila, mice, yeast and tissue culture systems. Rapamycin
treatment of yeast [112] drosophila [113], and mammalian cells [114] causes
cell cycle arrest, reduces cell size in drosophila [113] and mammalian cells
[115], and inhibits cap dependant translation [116] and ribosomal biogenesis
[117]. Elegant and thorough work by Fingar et al. [115] demonstrated the
critical role mTORC1 plays in regulating cell size through S6K1 and 4EBP.
These authors overexpressed various mutants of mTORC1, S6K1, eIF4E and
4EBP in a range of mammalian cell types with and without the mTORC1
inhibitor, rapamycin and the PI3K inhibitor, LY294002. Using flow cytometeric
analysis to measure cell size, and biochemical analysis of downstream signalling
events they showed that in all cell types, and cell cycle phases rapamycin
blocked activation of S6K1, phosphorylation of 4EBP and increased
association of 4EBP with eIF4E resulting in reduced cell size. The reduction
of cell size was modest, approximately 10% and overall cellular protein
content was reduced by approximately 30%. Overexpression of rapamycin
resistant (RR) mTORC1 in the presence of rapamycin rescued the effect of
rapamycin on signalling and cell size, however, neither RR-S6K1 nor eIF4E
alone elicited a similar rescue. Co-expression of both RR-S6K1 and eIF4E
together elicited a stronger rescue, however, full rescue was not achieved.

Molecular mechanisms of skeletal muscle hypertrophy
59
Transcriptional profiling of yeast [118] and mammalian cells [119]
treated with rapamycin revealed that TOR/mTORC1 also controls the
transcription of ribosomal genes. mTORC1 can regulate transcription by the
three classes of RNA polymerases [Pol-I (controls rRNA synthesis), Pol-II
(controls ribosomal protein genes) and Pol-III (controls 5S rRNA synthesis)]
which control the transcription of ribosomal components [120]. In cultured
drosophila cells, rapamycin, RNAi and transcriptional profiling identified
regulators of ribosomal biogenesis, and ribosomal assembly as genes regulated
by dTOR [113]. Interestingly, as knock down of dIF4E and dS6K together did
not reduce cell size to the same extent as rapamycin, this suggests that TOR has
other outputs that control ribosomal biogenesis and cell size [113]. Together,
these data demonstrate that mTORC1, through S6K1, 4EBP, as well as other
unknown effectors, plays an important role in regulating cell size and cell
cycle progression by exerting effects on translation initiation and the rate of
ribosome biogenesis.
2.3 S6K1
As discussed above, mTORC1 controls cell size largely through S6K1 and
eIF4E/4EBP. S6K1 is a member of the AGC family of serine/threonine kinases
that are dependent on phosphoinositide dependent protein kinase-1 (PDK1) for
activation [121]. While S6K1 can be phosphorylated by a number of other
kinases, including mitogen activated protein kinase (MAPK) and protein kinase-
C (PKC), it is dependent on mTORC1 [122] and PDK1 [123,124] for full
activity. mTORC1 associates with S6K1 through raptor [109], which not only
binds to the TOS motif in S6K1 but also suppresses an inhibitory function in the
C-terminal motif (RSPRR) of S6K1 allowing for phosphorylation at Thr389
(mTORC1 site) and Thr229 (PDK1 site) [125]. Mammalian cells express two
splice variants of S6K1 termed p85S6K [126], predominantly localised to the
nucleus [127], and p70S6K, which is predominantly cytoplasmic [127]. There
also exists a separate homologue of S6K1 referred to as S6K2, which is the
product of a separate gene but does not appear to regulate cell size [128].
Knock out of S6K1 in mice [128] and dS6K in drosophila [129] results in
a small organism phenotype, the transgenic mice or drosophila being ~15%
smaller than controls. Analysis of cells cultivated from these organisms
reveals that the reduced body size is a consequence of a reduced cell size, as
cell number and proliferation remains the same [128,129]. Skeletal muscle
cells cultured from S6K1-/- mice were approximately 20% smaller and were
unable to increase myotube diameter normally in response to IGF-1 and
constitutively active PKB [130]. Park et al. [131] made similar observations
in C2C12 cells treated with rapamycin or overexpressing mutants of mTORC1

D. Lee Hamilton et al.
60
and S6K1. These experiments demonstrated that S6K1 was required for
growth factor-induced skeletal muscle hypertrophy in vitro.
The requirement for S6K1 may result from its role in regulating
initiation, elongation, the capacity for translation, and/or nuclear export. S6K1
regulates elongation indirectly by phosphorylating and inhibiting elongation
factor 2 kinase (eEF2K) [132]. In basal conditions, eEF2K phosphorylates and
inactivates eEF2 slowing elongation [133]. S6K1 removes this inhibition
promoting translocation in the step phase of the ribosome [134]. However,
since elongation is not the rate limiting step of translation [135], increasing the
rate of elongation alone will not significantly increase the rate of protein
synthesis without a concomitant increase in initiation [135].
S6K1 can regulate initiation directly through eIF4B [136,137]. eIF4B
promotes the unwinding of the 5'-untranslated region (UTR) of mRNA [138-
140]. Phosphorylation of eIF4B by S6K1 increases its activity. When non-
phosphorylated eIF4B or a non-phosphorylatable S6K1 are overexpressed,
eIF4B binding to the initiation complex and translation are inhibited,
suggesting that the ratio of non-phosphorylated to phosphorylated eIF4B may
provide a mechanism for controlling translation. This is especially true for
mRNA containing a high degree of secondary structure in their 5'UTR such
as growth factors and protooncogenes [81]. While eEF2 and eIF4B are
clearly regulated by S6K1, they are not independent regulators of cell size
and are not exclusive targets of S6K1.
The best studied mechanism proposed for how S6K1 may regulate
translation and cell size is through the selective upregulation of the
translational machinery [141]. All ribosomal proteins characterised contain a
region in their 5'UTR that is rich in pyrimidines (5'TOP) [142]. In growth-
arrested cells, these transcripts are selectively repressed, but in growing cells
or upon mitogen stimulation, these transcripts shift to the polysome fraction
[143], thus increasing the expression of the translational machinery and the
protein synthetic capacity of the cell. In a series of elegant experiments
Jefferies et al. [143] and Terada et al. [117] demonstrated a correlation
between mTORC1 activity, S6K1 activation, S6 phosphorylation and the
translation of 5'TOP transcripts. Jefferies later showed [144] that a dominant
negative S6K1 could repress the translation of 5'TOP transcripts in a similar
manner to rapamycin and that a rapamycin resistant S6K1 could rescue cells
from the effects of rapamycin. These data strongly suggested a model
whereby phosphorylation of S6 by S6K1 improved the translational
efficiency of the transcripts containing a 5'TOP.
However, the role of S6 phosphorylation in 5'TOP translation has
recently been questioned [145-147]. Work from the Meyuhas lab utilizing the
PI3K inhibitor LY294002 and rapamycin suggested that S6K1 and S6

Molecular mechanisms of skeletal muscle hypertrophy
61
phosphorylation were not required for the improved translation of 5'TOP
transcripts [146,147]. However, the most convincing evidence against this
model has come from transgenic mice. Disruption of either S6K1 or S6K1
and 2 resulted in no difference in the rapamycin-sensitive effects of mitogens
on S6 phosphorylation or the translation of 5'TOP transcripts [128,148],
proving that S6K1 is not required for 5'TOP regulation. Furthermore, knock
in mice expressing a non-phosphorylatible form of S6 were still able to
promote the translation of 5'TOP RNAs [145]. Therefore, although increased
S6K1 activity can selectively promote the translation of RNA containing a
5'TOP, the underlying mechanism is currently unknown.
Another potential way S6K1 could regulate ribosomal biogenesis is by
regulating the transcriptional activity of upstream binding factor (UBF)
[149,150]. UBF binds to human selectivity factor-1 (SL1) and enhances the
activity of the ribosomal DNA (rDNA) transcriptional machinery, increasing
the ribosomal content of the cell [151]. S6K1 is required for growth factor-
induced increases in UBF phosphorylation and rDNA transcription [149].
However, no evidence for a direct interaction between UBF and S6K1 has been
found. Zhang et al. [150], have also shown that PKB/mTORC1/S6K1
signalling regulates RNA polymerase-1 dependent transcription [144],
suggesting another important role for S6K1 in regulating ribosomal biogenesis.
The last way that S6K1 may control translation is by regulating nuclear
export of mRNA. The only factor to date that is a specific target of S6K1 and
not S6K2 is the Aly/REF like protein, SKAR (S6K1 Aly/REF like target)
[152]. While the molecular function of SKAR has yet to be elucidated, the
homology between SKAR and Aly/REF suggests that it might be involved in
mRNA splicing or possibly mRNA export [153].
To summarise, through S6K1, mTORC1 regulates protein initiation
through eIF4B phosphorylation, elongation through eEF2K phosphorylation,
ribosomal biogenesis at the level of rDNA transcription through regulation of
UBF and 5'TOP translation, and potentially regulates mRNA processing and
export through SKAR.
2.4 eIF4E - 4EBP
Like S6K1, eIF4E can control translation through the regulation of
initiation, ribosomal biogenesis, and/or the export of mRNA from the
nucleus. Unlike S6K1, overexpression of eIF4E alone can lead to cellular
transformation or aberrant growth in a number of cell lines [154-156] and
higher levels of eIF4E are found in a range of cancers [157]. Knock down of
eIF4E with expression of antisense RNA reduces global translation rates and
lengthens cell division [158]. Together, these data suggest that, like S6K1,
eIF4E is an important regulator of cell growth.

D. Lee Hamilton et al.
62
Regulation of eIF4E occurs at two levels; direct phosphorylation and
sequestration by the 4EBPs. Phosphorylation of eIF4E is associated with
growth and activation of translation, but the mechanism depends on the site
of phosphorylation. Phosphorylation by PKC or protease activated kinase II
(a ribosomal S6 kinase) at Ser53 increases the association of mRNA and
eIF4E with the 43S preinitiation complex and increases translation initiation.
Phosphorylation at Ser209 by MAPK-activated protein kinase (MNK) on the
other hand promotes growth by stimulating the export of growth-related mRNA
(such as ornathine decarboxylase (ODC) [159], vascular endothelial growth
factor (VEGF) [160], and cyclin D1 [161,162] from the nucleus. However,
while knockout of MNK1 and MNK2 eliminates eIF4E phosphorylation at
Ser209, this does not alter cell growth or development in mice. In drosophila by
contrast, phosphorylation of eIF4E on Ser251 (equivalent to mammalian
Ser209) is absolutely required for normal growth and development [163].
These data show that eIF4E phosphorylation can lead to growth in an
mTORC1-independent manner. However, whether phosphorylation is an
essential control mechanism in mammals remains to be demonstrated.
A better characterized mechanism for regulating eIF4E activity is
through the 4EBPs. 4EBP1/2 bind to and inhibit the function of eIF4E [164]
in an mTORC1-dependant manner [116,165-170]. The interaction of the
4EBPs with eIF4E is dependant on a conserved sequence of 12 amino acids
that is found also in eIF4G, suggesting that the non-phosphorylated form of
4EBP competitively inhibits the binding of eIF4E to eIF4G. mTORC1
phosphorylates 4EBP in response to insulin and other growth stimuli and this
phosphorylation leads to a reduction in the interaction between eIF4E and
4EBP thus allowing eIF4E to bind eIF4G and promote 43S preinitiation
complex formation.
Liberation of eIF4E from the 4EBPs might also promote the translocation
of eIF4E into the nucleus. Recently, a nuclear import protein called the eIF4E
transporter (4E-T) has been identified and cloned [171], suggesting an active
shuttling of free eIF4E into the nucleus. Translocation of eIF4E to the
nucleus might be important in growth since mutation of Trp73 on the dorsal
surface of eIF4E prevents binding of either eIF4G or the 4EBPs, but retains
the capacity to increase cyclin D1 mRNA in the cytoplasm and transform
cells [172]. If free eIF4E is required for this growth effect, then mTORC1
would regulate this through phosphorylation of 4EBP. This has been
suggested in L6 cells where growth factor-induced hypertrophy results in
increased expression of cyclin D1 in a rapamycin sensitive manner [173].
While these data suggest an important role for 4EBP in controlling
growth, in contrast to the S6K transgenic mice, knockout of 4EBP1 alone
[174] or 4EBP1 and 4EBP2 [175] does not lead to increased animal size or

Molecular mechanisms of skeletal muscle hypertrophy
63
tumour formation. In fact loss of 4EBP leads to a small animal phenotype in
male mice [174], and the double knockout results in accelerated diet-induced
insulin resistance [175]. Both of these findings can be explained if the 4EBPs
act as competitive inhibitors of S6K1. Raptor binding to 4EBP decreases the
basal activity of S6K1. In the absence of the 4EBPs, S6K1 activity increases
leading to feedback inhibition of the insulin receptor substrates (IRS;
discussed below) and insulin resistance.
2.5 mTORC1 and resistance exercise
Baar and Esser were the first to suggest a role for mTORC1 in muscle
hypertrophy [80]. They demonstrated an increase in S6K1 phosphorylation, as
measured by gel shift analysis, from 3-36 hrs after a single bout of resistance
exercise. More interestingly, the degree of hypertrophy following 6 weeks of
training correlated with the degree of S6K1 phosphorylation 6 hours following
resistance exercise [80]. These findings have been confirmed by a number of
independent groups in a number of different models from rat to man. Not only
is S6K1 activity increased following electrical stimulation, but also by ablation
overload [59], passive stretch ex vivo [88] operant conditioning in rats [176],
and resistance exercise in humans [93,177]. Interestingly, the correlation
between S6K1 phosphorylation and hypertrophy in rats has also been
confirmed in humans. The increase in strength, fat-free mass, and fibre cross-
sectional area after 14 weeks of training all correlated with the phosphorylation
of S6K1 30 minutes after a single bout of resistance exercise [94]. This
indicates that S6K1 is a great marker for muscle growth and suggests that
mTORC1 activation might be important in the hypertrophic response.
Unlike S6K1, changes in eIF4E phosphorylation have never been
demonstrated in response to resistance exercise [176]. On the other hand, the
mTORC1 dependant phosphorylation [176] and inhibition of 4EBP increases
significantly in response to resistance exercise in the rat [89,178]. Synergist
ablation overload of the rat plantaris shifts the association of eIF4E from
4EBP to eIF4G in a rapamycin sensitive manner [59]. Furthermore, the
mTORC1 dependent increase in cyclin D1 expression during skeletal muscle
hypertrophy in vitro suggests that the liberation of eIF4E from 4EBP may
also promote growth by increasing eIF4E in the nucleus.
Together, these data suggest that mTORC1 regulates work-induced
muscle growth through S6K1 and 4EBP/eIF4E. These proteins selectively
increase the translation of important growth response genes such as cyclin D1
[173], eIF2B [179], and ribosomal proteins [144] resulting in an overall
increase in translation and ribosomal biogenesis (Figure 2). Repeating the
exercise stimulus at a sufficient frequency would result in greater protein
accretion and muscle hypertrophy.

D. Lee Hamilton et al.
64
Figure 2. Schematic representation of the activation of mTORC1 by resistance
exercise. Note that the activation of mTORC1 by integrin-linked kinase-induced PKB
phophosylation and/or MAP4K3 are at this point hypothetical. However, the role of the
other factors including phosphatidic acid and Vps34 have been seen experimentally.
3. Regulation of mTORC1
3.1 Endocrine regulation of mTORC1
Skeletal muscle expresses and responds to a range of endocrine signals.
As we have previously discussed, skeletal muscle expresses a range of
growth factors in response to load. Recently a variety of cytokines, termed
myokines, have been found to be expressed by skeletal muscle in response to
exercise [180,181]. Several of the myokines have been implicated in
mediating muscle growth. For instance IL6 (interleukin 6) [182], IL4 [183],
and IL15 [184-187] have all been implicated in muscle growth and IL6 [182],
and IL15 [188] are regulated following load or exercise. However, due to
space limitations we will focus on the mode of activation of mTORC1. For
instance mTORC1 is one of the primary mediators through which insulin
[189] and IGFs [130,131,190] exert their influence on anabolism. β2-
adrenergic agonists such as clenbuterol [191] are also capable of activating
mTORC1 and inducing muscle growth. Evidence also suggests a role for Wnt
signals in the regulation of mTORC1 [192] suggesting that mTORC1 serves
as a control point for many of the growth factors that affect muscle size.

Molecular mechanisms of skeletal muscle hypertrophy
65
Growth factor regulation of mTORC1
Canonical insulin and growth factor signalling begins with ligand
binding to a membrane receptor [193,194]. The insulin and IGF-1 receptors
are hetero-tetrameric proteins consisting of two identical α-subunits that
protrude into the extracellular space anchored to the membrane by their
association with the two identical β-subunits that project into the intracellular
space. The β-subunits possess intrinsic tyrosine kinase activity, which is
stimulated upon ligand binding to the α-subunits resulting in auto
phosphorylation of the intracellular subunits on tyrosine residues. Tyrosine
phosphorylation recruits scaffolding proteins such as members of the insulin
receptor substrate (IRS) family and Shc (Src homology collagen) proteins
which themselves become tyrosine phosphorylated [195,196]. The IRS
proteins recruit class I PI3Ks to the membrane via the SH2 (Src homology 2)
domain in the p85 subunit of the PI3K complex leading to the generation of
phosphatidylinositol-3,4,5-triphosphate (PIP3) from phosphatidylinositol-4,5-
biphosphate (PIP2) in the plasma membrane [197]. The increase in PIP3
leads to the recruitment of phosphoinositide dependant kinase (PDK)1 and
PKB/akt to the membrane through their plekstrin homology (PH) domains,
which bind PIP3. Binding of PIP3 leads to a conformational change in PKB
allowing it to be phosphorylated by PDK1 at Thr308 [121,198] and mTORC2
on Ser473, resulting in a fully active kinase [111]. PKB can then directly
activate mTORC1 [199] through phosphorylation at Ser2448 [200], or
indirectly activate mTORC1 by destabilising the inhibitory tuberous sclerosis
complex (TSC)1/2 through phosphorylation of TSC2 [201]. The TSC1/2
complex acts as a GTPase activating protein (GAP) towards the small
GTPase Rheb (Ras homologue enriched in brain) [202]. When Rheb is in the
GTP bound state it acts through an unknown mechanism to activate mTOR
[202-204]. Receptor tyrosine kinase phosphorylation also leads to the
activation of the Ras/Raf/MAPK pathway [195], which can also inhibit
TSC1/2 and activate mTORC1 through RSK [205]. To summarise, growth
factors interact with membrane bound receptors and initiate a series of
signalling cascades leading to either the direct phosphorylation and activation
of mTORC1 or the inhibition of TSC1/2 GAP activity resulting in a greater
Rheb-GTP and subsequent mTORC1 activation.
3.2 Energy status
Seminal work by Hickson, [206] demonstrated that concurrent training to
improve strength and endurance inhibited strength gains, and actually led to a
decline in strength after ten weeks. The possible causes for this are well
reviewed by Nader, [207], but here we will discuss how energy status can
signal to regulate mTORC1 activity. A key enzyme involved in energy sensing

D. Lee Hamilton et al.
66
is the adenosine monophosphate dependent protein kinase (AMPK). AMPK
senses cellular energy status by responding to the ratio of AMP/ATP [208].
When cellular AMP concentration increases, AMP binds to AMPK, allosterically
activates the enzyme, and makes it a better substrate for its upstream kinases
LKB1 or the calcium calmodulin-activated protein kinase kinases (CaMKK)
and [209-211]. The upstream kinases phosphorylate AMPK at Thr172
leading to full activation [212]. Active AMPK inhibits anabolic processes and
activates catabolic processes that enhance ATP generation [213]. One of the
key anabolic processes that is inhibited by AMPK is protein synthesis.
Activating AMPK using the AMP mimetic 5-aminoimidazole-4-carboxamide
1-beta-d-ribonucleoside (AICAR) in rats decreases protein synthesis by
reducing mTORC1 signalling [214]. The molecular mechanism for this was
elucidated by Inoki et al. [215] who showed that under conditions of energy
stress, TSC2 was phosphorylated and activated by AMPK thus inhibiting
mTORC1 activity and decreasing protein synthesis. Therefore, any process
that leads to energy stress can lead to the activation of AMPK and subsequent
inhibition of mTORC1 and protein synthesis. One physiological stimulus that
leads to the activation of AMPK is endurance exercise [216] and the
activation of AMPK is thought to be one of the key molecular mechanisms
underlying the adaptation to endurance exercise [217]. As a result, endurance
exercise likely decreases the hypertrophic response by activation of AMPK
and a subsequent reduction in the activation of mTORC1.
Energy stress in the form of hypoxia is also capable of modulating
mTORC1 activity. Hypoxia inhibits overall translation rates and increases the
association of eIF4E with 4EBP1 [218] by modulating mTORC1 activity.
The decrease in mTORC1 activity is the result of an increase in expression of
the hypoxia inducible gene REDD1 [219]. REDD1 inhibits mTORC1 by
activating TSC1/2 [220] in an AMPK-independent manner [218]. Interestingly,
following treatment with protein synthesis inhibitors, mTORC1 activity is
rapidly increased. This increase in mTORC1 appears to be due to the rapid
degradation of REDD1 following the treatment with inhibitors of protein
synthesis [221]. The degradation of REDD1 results in TSC1/2 inhibition and
mTORC1 activation. A similar model is possible for hypoxia. At the
cessation of hypoxia, REDD1 would be rapidly degraded potentially leading
to mTORC1 activation. This might explain recent work showing a positive
effect of resistance training with venous blood flow restriction [222]. Although
hypoxia is not normally a consequence of resistance training, very light
resistance exercise during hypoxia results in skeletal muscle hypertrophy in
both rats and humans [223]. Interestingly, following training in the hypoxic
state there is a disproportionately large increase in mTORC1 activity [224].
One possible explanation for the ability of light exercise and hypoxia to

Molecular mechanisms of skeletal muscle hypertrophy
67
activate mTORC1 is that exercise in the hypoxic state results in a large
increase in REDD1 during the exercise bout resulting in inhibition of protein
synthesis. Immediately following the hypoxic exercise, there is a rapid decrease
in REDD1 leading to a compensatory increase in mTORC1 activity after the
exercise bout driving an increase in protein synthesis and muscle hypertrophy.
3.3 Nutrient regulation
Increased muscle size and strength are optimised by the combination of
resistance exercise and appropriate nutrition. Integration of the two stimuli
likely occurs at the level of mTORC1. However, how amino acids work
upstream of mTORC1 remains to be fully elucidated. Following a single bout
of resistance exercise, both protein synthesis and protein degradation are
increased [225]. In the fasted state, the protein balance is negative with
protein breakdown exceeding protein synthesis. This balance is partially
recovered following resistance exercise due to an increase in protein
synthesis [13,14,226]. However, in the fasted state there is a concomitant
increase in protein degradation so the net balance remains negative. Net
balance becomes positive only when nutrients are added. Here, it is the
addition of protein, specifically essential amino acids that has the strongest
effect. It also appears that the timing of the supplement also plays a role in
the determination of net protein balance. Ingestion of an amino acid-
carbohydrate mix immediately before or within one hour of an exercise bout
greatly enhances the degree of protein synthesis and reduces breakdown
thereby maximising muscle growth [20,227,228].
The mechanisms by which amino acid signalling induces cell growth
differ depending on the amino acid studied [229]. Some amino acids induce
cell swelling indirectly through an increase in cell osmolarity following
sodium ion dependent amino acid transport. Another group, including amino
acids like leucine promote cell growth by activating protein synthesis [230-
232]. Leucine is a branched chain amino acid (BCAA) along with valine and
isoleucine. BCAAs, particularly leucine, are very potent anabolic agents and
effectively increase the activity of mTORC1 [233]. Rapamycin partially
inhibits the ability of leucine to increase protein synthesis. The partial
inhibition suggests an as yet unidentified mTORC1 independent pathway
[230,234]. Recently, two amino acid sensitive pathways have been identified
upstream of mTORC1. The first is the class 3 phosphoinositol kinase Vps34
identified by Byfield and colleagues [235]. Vps34 can be inhibited in the
absence of amino acids and may be permissive in the activation of mTORC1.
Vps34 also plays a role in protein degradation in the form of autophagy with
its binding partners Vps15 and Beclin1 [236,237]. This suggests that Vps34
might underlie the correlation between protein degradation and protein

D. Lee Hamilton et al.
68
synthesis observed by Phillips and his colleagues [225]. The second potential
amino acid sensor is mitogen-activated protein kinase kinase kinase kinase
(MAP4K)-3. Like Vps34, MAP4K3 is activated by the administration of
amino acids [238]. MAP4K3 is also required for the activation of mTORC1
and when MAP4K3 is overexpressed in HEK-293 cells there is an increase in
mTORC1 activity. Interestingly, MAP4K3 knockdown results in a decrease
in cell size suggesting that it can directly affect the anabolic state within cells.
Amino acid consumption in parallel with resistance exercise can
maximise muscle growth [20,228]. As reviewed by Tipton and Witard [239]
both the timing and the type of amino acids also affect post exercise protein
synthesis. Most of the data suggests that amino acid uptake into muscle and
the subsequent increase in protein synthesis is highest when the supplement is
consumed immediately before or within an hour of completing exercise. The
effect of the timing might be the result of a window of increased amino acid
uptake associated with resistance exercise. Indeed, amino acid uptake is
increased to a greater degree when the amino acids are taken before the
exercise bout, likely due to the shunting of blood to the active muscle.
Furthermore, muscle stretch results in the activation of amino acid uptake [76]
and this increase in amino acid uptake is required for the activation of protein
synthesis [75]. This suggests that resistance exercise promotes amino acid
uptake and this increase in amino acid uptake is important for the activation of
mTORC1, either though Vps34 or MAP4K3, and skeletal muscle hypertrophy.
3.4 Mechanical stimulation
As discussed above, amino acids enhance protein synthesis in response to
stretch but do not appear to initiate mTORC1 activation. Little is known
about how mTORC1 is initially activated by mechanical stimuli. Wortmannin, a
potent inhibitor of PI3K, has no effect on stretch activated p70S6K(T389)
phosphorylation suggesting that PI3K is not required [87,88]. Even though
PI3K is not activated by stretch, PKB/akt, a downstream target of PI3K, can
be activated immediately after resistance exercise/high frequency stimulation
of muscle [59,240,241]. Therefore, there is either a PI3K-independent
mechanism for activating PKB/akt or the activation of PKB/akt is not required
for mTORC1 activation. One PI3K-independent mechanism for activating
PKB/akt, through the integrin-linked kinase, has been described above.
Hornberger and his colleagues [88] have suggested an alternative mechanism
for mechanically-induced signalling through mTORC1. First they showed that
mTORC1 is still activated in PKB-/- mice. In addition, using whole muscle in-
vitro stretch they demonstrated that autocrine/paracrine factors did not
activate mTORC1, suggesting that PKB/akt was not required for mTORC1
activation. In a follow up study, they described a sustained increase (15-90

Molecular mechanisms of skeletal muscle hypertrophy
69
minutes post) in the lipid second messenger phosphatidic acid (PA) in response
to mechanical stimulation [242], confirming work performed some 17 years
previous [243]. Two chemically distinct inhibitors of phospholipase D (PLD),
the enzyme that catalyses the production of PA and choline from the
phosphotydlcholine, blocked the mechanical activation of mTORC1 and the
production of PA in response to stretch. These data suggest PLD forms part of
the pathway from mechanical stimulation to mTORC1. Indeed, PA has already
been proposed as a critical signalling molecule in cardiac hypertrophy [244]
and stretch can lead to rapamycin-independent activation of mTORC1 [242].
This occurs since PA competes with rapamycin for binding to the FRB domain
of mTOR [245]. There are, however, several other enzymatic pathways that
regulate PA both positively and negatively that may play a role in the activation
of mTORC1. One is diacylglycerol kinase (DGK), the enzyme that converts
diacylglycerol (DAG) into PA [246,247]. Another, is phospholipase A (PLA)
the enzyme that reduces PA to lysophosphatidic acid and free fatty acids
through deacetylation [248]. The last is the PA phosphatase (PAP) that converts
PA into DAG [249]. However, the involvement of these enzymes has yet to be
investigated in the mechanical regulation of mTORC1. It is clear that PA can
activate mTORC1 following mechanical stretch, but it remains to be
definitively determined how this occurs.
3.5 Negative feedback
Tremblay & Marette [250] were the first to demonstrate a negative
feedback loop in the insulin signalling pathway. They found that incubating
L6 cells in high concentrations of amino acids for 1 hour could reduce
insulin-induced 2-deoxyglucose (2-DG) uptake. This effect was caused by
increased IRS-1 phosphorylation and subsequent degradation, leading to
decreased PKB activity. This response was rapamycin sensitive suggesting
that mTORC1, or a mTORC1 target caused it. Because the response was
correlated to S6K1 phosphorylation they hypothesised that it was mediated
by S6K1. Consistent with this, loss of dTSC1/2 in drosophila leads to a
disruption in insulin signalling which is rescued by RNAi against dS6K
[251]. Later work by Harrington et al. [252] in TSC2-/- (tuberous sclerosis
complex-2) mouse embryonic fibroblasts (MEFs), which have constitutive
mTORC1 signalling, confirmed these earlier findings. This study utilized
mutants of S6K1 and IRS-1 to demonstrate a direct interaction between S6K1
and IRS-1 both at the protein and mRNA level. Microarray analysis of TSC2-
/- and wild type MEFs showed that IRS-1 mRNA was downregulated in the
absence of TSC2. This was confirmed with qPCR, and IRS-1 mRNA was
partially rescued with the use of rapamycin or RNAi against S6K1. IRS-1
protein was also suppressed, and this was rescued by re-expression of TSC2.

D. Lee Hamilton et al.
70
Next they screened fragments of IRS-1 using in vitro phosphorylation assays
with S6K1 and identified Ser302 as an in vitro phosphorylation site for S6K1.
TSC2-/- MEFs had higher IRS-1 Ser302 phosphorylation, and S6K1 RNAi
reduced this. Finally they showed that the S6K1 mediated phosphorylation of
IRS-1inhibited IRS-1 function and prevented growth factor mediated signalling,
thus further confirming the function of S6K1 as a negative regulator of
insulin signalling. More recently IRS-1 has been shown to be a direct target
for mTORC1 through the association of IRS-1 with raptor again resulting in
an inhibition of IRS-1 function [253]. This series of experiments helps to
explain the phenotype of 4EBP/2-/- mice, discussed above, and also that of
mice overexpressing IGF-1. Mice overexpressing IGF-1 under the control of
a myosin light chain promoter display hypertrophied muscle with increased
mTORC1/S6K1 phosphorylation and reduced PKB phosphorylation [72]. Few
studies have examined the effect of resistance training on this negative feedback
loop. However some have analysed the effect of resistance training on insulin
sensitivity, which is a good read out for the activity of this loop. 6 weeks of
overload-induced hypertrophy of the Extensor Digitorim Longus has no
effect on insulin mediated glucose disposal in rats [254] and long term
resistance training in young [255], and diabetic [256] subjects improves
insulin responsiveness. However there are confounding results regarding the
acute response. Howlett et al. [257] found a reduction in insulin signalling
immediately after a single bout of resistance exercise, this was associated with
a reduction in insulin stimulated IRS-1 tyrosine phosphorylation which could
suggest an increased serine phosphorylation [252]. Koopman et al. [258] found
that a single session of resistance exercise increased insulin sensitivity
measured 24 hrs post stimulation, however the mechanism is unclear. Although
this feedback loop is clearly important in nutrient regulation it is still unclear
whether it is physiologically relevant to resistance training or hypertrophy.
4. Transcriptional control
Prior to the current focus on protein synthesis and mTORC1 activity, a
great deal of early research focused on the importance of transcription in the
development of skeletal muscle hypertrophy. The absolute requirement for de
novo transcription in the development of skeletal muscle hypertrophy was
demonstrated using the transcriptional inhibitor actinomycin D to prevent
overload-induced hypertrophy of the plantaris in rats [77]. However, the
specific transcriptional events that are required for muscle growth remain
unknown. Part of the challenge in determining the transcriptional regulation
of muscle hypertrophy is the greater complexity of this aspect of growth in
comparison to translational regulation. This complexity is the result of the
fact that the acute changes in RNA synthesis can be very different than the

Molecular mechanisms of skeletal muscle hypertrophy
71
long-term changes in the expression profile within muscle [259]. Since the
acute changes associated with resistance exercise are thought to cause muscle
hypertrophy, this area has been more extensively studied. Most of these studies
including: Carson et al. [260] studying the effects of 3 days of overload on the
transcriptional profile in the soleus of rats; Chen et al [261] studying the
transcriptional changes 1-6hrs following a single bout of lengthening contractions
in rats; and Kostek et al [262] studying the transcription profile in humans 3-
24 hours after resistance exercise have shown that genes from functional
classifications such as; proliferation, autocrine/paracrine signalling, extracellular
matrix, immune response, metabolism, and protein synthesis/degradation are
effected by acute resistance exercise. The most striking finding from these
studies is that resistance exercise alters the expression of not just individual
genes but large clusters with functions as diverse as inflammation [263],
growth and differentiation [261,264], and protein degradation [265]. This
suggests either that there are specific transcription factors that can target large
gene clusters, or that the expression of these genes is coordinately controlled
by global transcriptional regulators like microRNAs.
Some transcription factors that are required for the activity of muscle-
specific promoters have been identified [266]. These include the myogenic
regulatory factors: Myf5, MyoD, myogenin, and MRF4, the myocyte
enhancing factor (MEF)2, serum response factor (SRF), and transcription
enhancer factor (TEF)1. The myogenic transcription factors myogenin [267-
269], MyoD [268,269] and MRF4 [269] are increased during load-induced
hypertrophy. In response to overload in the chicken, both SRF and TEF1
increase and this leads to an increase in skeletal -actin expression [270,271].
The increased expression of -myosin heavy chain (MHC) [272] during
overload hypertrophy in the plantaris muscle of rats is dependent on an A/T
rich element [273] and TEF1 binds to this element and can upregulate the
expression of the -MHC gene [274] suggesting that SRF and TEF1 might be
involved in the hypertrophic response. However, none of these genes have
been shown to be necessary for skeletal muscle hypertrophy.
A few transcriptional regulators are either sufficient or necessary for
skeletal muscle hypertrophy. Furthermore, these factors control the
expression of gene clusters making them likely candidates for mediating the
transcriptional response to load-induced skeletal muscle hypertrophy. As a
result, we have chosen to focus our attention on these factors, specifically
myostatin/ -catenin and microRNAs.
4.1 Myostatin / -catenin
Myostatin is a member of the transforming growth factor (TGF) family
that acts through the regulation of cell cycle genes [275], the myogenic

D. Lee Hamilton et al.
72
regulatory factors MyoD [276,277], and myogenin [277] to promote
myoblast proliferation and suppress differentiation [275,278]. Myostatin
initially gained interest when it was discovered to be the cause of the double
muscle phenotype seen in Belgian Blue cattle [279]. Without the influence of
myostatin during development, muscle growth is almost unrestrained and the
result is massive muscle hypertrophy in dogs, sheep, cattle [279,280] and
humans [281]. For more in depth reviews on myostatin and TGF signalling
see excellent reviews by Lee [282], Massague [283] and Tsuchida et al. [284].
Briefly Myostatin exerts a transcriptional influence through an interaction with
various membrane receptors such as the ActRIIB (activin receptor type IIB)
that ultimately leads to the phosphorylation of receptor regulated Smads (R-
Smads). When phopsphorylated, the R-Smads, in association with the
common mediator Smad4, translocate to the nucleus where they bind to the
Smad binding element and together with a variety of coactivators control the
transcription of various genes. The importance of this signalling pathway in
the determination of muscle size is evidenced by the fact that genetic
manipulation at various points in the pathway results in a hypertrophic
phenotype. For instance overexpression of a dominant negative ActRIIB
receptor, Follistatin (which inhibits myostatin function) [285] and Ski (which
inhibits Smad function) [286] all induce skeletal muscle hypertrophy.
Whether myostatin plays a key role in load-induced hypertrophy in humans
remains controversial. One study demonstrated that myostatin transcription is
down regulated 24hr after a single bout of resistance exercise in young men
and women, but the response is blunted in the aged [287]. Another study
demonstrated a reduction in the myostatin receptor (ActRIIB) with no change
in myostatin 1hr post resistance exercise [288]. Interestingly, basal myostatin
expression was increased by 21 weeks of resistance training, but after training
myostatin was acutely depressed 48hr after a single bout of resistance exercise
[288]. In rats, overload suppresses myostatin expression at 3 days (rapid
growth phase) but returns to normal levels by 21 days of overload (slower
growth phase). The response was retained in hyposectamized animals suggesting
it is independent of pituitary hormones [289]. One long-term hypertrophy study
in humans tried to correlate myostatin expression to the degree of hypertrophy
achieved after 16 weeks of resistance training [290]. They assessed the degree of
hypertrophy achieved by 66 subjects and assessed myostatin expression after
the first and last training bouts according to whether the subjects were
extreme, modest or non-responders. They showed that the change in
myostatin expression did not correlate with the development of hypertrophy.
One problem with interpreting the myostatin studies is that few of these
studies assess myostatin activity. Most of this work measures myostatin
mRNA changes with resistance exercise, which may not accurately depict

Molecular mechanisms of skeletal muscle hypertrophy
73
myostatin signalling. As described above, this pathway is regulated at a
number of different levels including myostatin level, receptor binding,
receptor level, and Smad signalling. Therefore, measuring the amount of
myostatin mRNA within a muscle is not sufficient to determine the activity
of this pathway. For instance, genomic analysis after resistance exercise has
identified extracellular matrix proteins as being increased. Since myostatin
plays an important role in the expression of ECM in muscle, this suggests
that myostatin signalling may be enhanced following exercise. Hopefully the
recent identification of Mighty as a direct target of myostatin activity [322]
will give us an accurate marker of the activity of the myostatin pathway
following resistance exercise and will definitively answer whether the
myostatin pathway is decreased by resistance exercise. There is no question
that myostatin regulation is essential for muscle development in both the pre-
and post-natal period, but the contribution of myostatin to skeletal muscle
hypertrophy has yet to be determined.
Genome-wide analysis of the myostatin knockout mouse identified the
Wnt/ -catenin signalling pathway as being disproportionately upregulated in
the absence of myostatin. -catenin is a multifunctional protein that participates
in cell adhesion and transcriptional regulation [291] and is an important
component of several signalling pathways. -catenin is at the centre of two
separate pathways with important roles in myogenesis: Wnt, which regulates
transcription during development [292]; and m-cadherin, a membrane associated
protein that regulates cell adhesion [293,294]. When Wnt signalling is activated,
as occurs developmentally and in cancer [295], -catenin is dephosphorylated as
a consequence of the inhibition of its upstream kinase glycogen synthase
kinase (GSK)3 and is able to translocate to the nucleus and regulate
transcription by association with a range of adapter proteins [296]. Cadherin-
catenin interactions are believed to link the cadherins to the actin cytoskeleton
[297] and deregulation of -catenin at the membrane results in defects in
myofirbrillogenesis [298]. When -catenin becomes transcriptionally active,
it upregulates the cell cycle regulators cyclin D1 [299,300] and c-myc
[299,301]. As discussed earlier cyclin D1 may have an important role in
skeletal muscle hypertrophy [173] and inducible overexpression of c-myc in
heart tissue in vivo induces cardiac hypertrophy [302].
The Wnt mediated function of -catenin is positively regulated by
intense endurance exercise in rats [303] and humans [304], and both the Wnt
and cadherin functions are regulated during overload-induced hypertrophy in
rats and mice [305,306]. The co-localization of -catenin with M-cadherin
during overload hypertrophy in rats is increased [305]. Overload of the
mouse plantaris also increases the expression of several proteins in the -
catenin pathway, including frizzled, dishevelled-1 and lymphocyte

D. Lee Hamilton et al.
74
enhancement factor (LEF-1) [306]. There was also an increase in the
association of GSK3 with its inhibitor FRAT resulting in stabilisation of -
catenin, nuclear translocation, and association with LEF-1 in the nucleus. The
increased -catenin-LEF-1 association resulted in a corresponding increase in
the expression of the transcriptional targets c-myc and CyclinD1 [306]. An
independent microarray study also identified the upregulation of the Wnt
receptor Frizzled after 3 days of overload in rat soleus muscles suggesting
that the activation of Wnt/ -catenin is a universal response to load-induced
hypertrophy [260]. In support of this hypothesis, utilizing an adenovirus to
knockdown -catenin in mice, Armstrong et al. demonstrated that -catenin
is essential for overload-induced hypertrophy (Figure 3; [307]). These data
strongly support a role for -catenin in hypertrophy and raise the possibility
that myostatin and -catenin may act antagonistically on muscle mass by
regulating a subset of genes that are critical for muscle growth. However, the
identity of these genes remains to be determined.
Recently, the interaction between the Wnt/ -catenin pathway and
mTORC1 has been demonstrated both in cells and in human tumours. Inoki
et al. [192] were the first to show that Wnt signalling could activate mTORC1
by regulating the phosphorylation and activity of TSC2. In the absence of
Figure 3. The requirement for -catenin in skeletal muscle hypertrophy is seen in the
lack of growth in fibres where viral knockdown of this protein has occurred. Viral
infection is seen as green cells, while -catenin is stained red. Note the smaller size of
the -catenin null cells after overload (from [307]).

Molecular mechanisms of skeletal muscle hypertrophy
75
Wnt, GSK3 phosphorylated and activated TSC2. The addition of Wnt to Rat1
fibroblasts resulted in sequestration of GSK3, decreased TSC2 phosphorylation,
and increased mTORC1 activation. Interestingly, a follow up study in tumours
from tuberous sclerosis patients, where mTORC1 activity is elevated, showed
that activation of mTORC1 led to increased -catenin levels and elevated
c-myc and cyclin D1 expression [308]. This suggests that a positive feedback
mechanism might exist between mTORC1 and Wnt/ -catenin signalling and
this may play a role in the development of skeletal muscle hypertrophy.
4.2 microRNA
While the transcription factors discussed so far bind to the promoter
region of their target genes and together with coactivators regulate
transcription, micro (mi)RNAs use a much different molecular mechanism to
control the expression of large families of mRNA. miRNA are non-coding
short chains of mRNA approximately 22nt long. They bind to complementary
sequences in the 3’UTR (untranslated region) of target transcripts repressing
the translation and promoting the degradation of these mRNAs [309]. To
date, the mir-Base catalog of miRNAs contains 5395 entries across 36
different species [310], with approximately 800 predicted in the human
genome [311]. Remarkably these miRNAs are estimated to regulate up to
30% of all human genes [312]. miRNA processing is required for normal
development [313] and, when overexpressed, miRNAs have the potential to
vastly alter the transcription profile of a cell. For instance when the muscle
specific mir-1 is overexpressed in HeLa cells the expression profile, as
measured by microarray, is shifted towards that of skeletal muscle [314].
Several studies have shown muscle specific miRNAs to be regulated during,
and important for, muscle development. For instance, the muscle specific
microRNAs mir-1 [315], mir-206 [316] and mir-181 [317] are induced during
in vitro myogenesis in C2C12 myoblasts and promote muscle differentiation
by downregulating the expression of HDAC4 (histone deacetylase-4),
connexin43 [318], and Hox-A11 respectively. mir-133 is on the same cistron
as mir-1 and is also strongly induced during myogenesis, but strangely is
antagonistic, in that it promotes myoblast proliferation by suppressing SRF
(serum response factor) [315]. The expression of both mir-1 and mir-133 is
controlled by MEF2 and the myogenic regulatory factors through regulatory
elements upstream of the biscistronic miR-1-1/133a-2 message [319].
Interestingly, in response to overload hypertrophy in mice the expression of
the unprocessed pri-mir-1, pri-mir-133a and pri-mir-206 are increased.
However, the mature mir-1 and mir-133a are suppressed while mir-206
remains unchanged (Figure 4; [320]). The regulation of mir-133a in response
to overload is interesting as SRF signalling is required for the regulation of

D. Lee Hamilton et al.
76
skeletal α-actin gene during stretch-induced hypertrophy of the chicken
anterior latisumus dorsi [270,321]. This data suggests that miRNAs could be
involved in altering the transcription profile required for hypertrophy. This
hypothesis is extremely interesting as the modulation of a single miRNA can
vastly alter the transcription profile of the cell and it is possible that alterations
in a few miRNAs could be responsible for the large shift in transcription
profiles seen after resistance exercise.
While the possibility exists that miRNAs are involved in the regulation
of muscle size following resistance exercise, how resistance exercise
regulates the expression of specific miRNAs is still completely unknown.
However, the increased interest in understanding the mechanisms underlying
miRNA regulation should mean that a number of tools will quickly come
online to directly study this question in vivo.
Figure 4. A decrease in the muscle-specific miRNAs mir-1 and 133a following 7 days
of functional overload (from [320]). Note also the trend towards increased mir-206
that might be important in the shift from fast to slow muscle fibres.
5. Future perspectives
The last 40 years of research have given us an understanding of some
of the key players in resistance exercise-induced skeletal muscle
hypertrophy but, we still do not know the extent that each contributes to

Molecular mechanisms of skeletal muscle hypertrophy
77
in vivo muscle growth. However, the field of skeletal muscle hypertrophy
is expanding rapidly, with new techniques and technologies available old
problems are now accessible. In the final section we will present some of
the most pressing questions that remain and set out a methodological
paradigm for investigating the role of a particular factor in the development
of skeletal muscle hypertrophy.
1. How is mTORC1 activated following resistance exercise?
As discussed above, how mTORC1 is activated in response to resistance
exercise is still controversial. In the heart it seems clear that costameres play
a role in the mechanical link between stretch and mTORC1 since ILK [55],
melusin [56,57], and the muscle LIM domain protein [58] all co-locate with
protein kinase B/akt in costameres and are required for mechanosensing.
Whether a similar system is used in skeletal muscle has yet to be determined.
Interestingly, in all of the microarray studies performed following resistance
exercise the muscle LIM domain protein is upregulated. This suggests that
the costameric structure is affected by resistance exercise and may play an
important role in mechanotransduction.
2. Does the activation of mTORC1 lead to skeletal muscle
hypertrophy?
Clearly, the activation of mTORC1 correlates with muscle hypertrophy.
However, experiments designed to demonstrate that mTORC1 can drive
skeletal muscle hypertrophy have been unsuccessful [59]. This may indicate
that mTORC1 is not sufficient for muscle hypertrophy or it could indicate
that the feedback inhibition of IRS-1 and PKB prevents muscle cell growth.
In order to determine the role of mTORC1, a transient expression system is
necessary. In this way, the effect of repeated transient activation of mTORC1
on muscle cell size can be determined.
3. Does mTORC1 activation lead to -catenin activation?
As described above, tumours from tuberous sclerosis patients have
increased -catenin levels suggesting that the activation of mTORC1 can
lead to the accumulation of -catenin. Since mTORC1 activation and
-catenin signalling are the two pathways that appear to be necessary for
skeletal muscle hypertrophy, mTORC1-dependant activation of -catenin
would provide a unifying molecular mechanism for skeletal muscle hypertrophy.
Therefore, the affects of acute activation of mTORC1 on -catenin levels in
muscle in the presence and absence of rapamycin need to be determined.

D. Lee Hamilton et al.
78
4. Is myostatin signalling regulated following resistance
exercise?
As described above, the role of myostatin in load-induced skeletal
muscle hypertrophy remains uncertain. This is predominantly due to the fact
the myostatin signalling is controlled at multiple levels and it is difficult to
know what to measure. With the identification of Mighty as a specific
myostatin pathway target gene [322], this gene can be used as a marker for
the activity of the whole pathway. As a result, the expression of Mighty
following resistance exercise will be a definitive measure of the activity of
mysostatin and will finally answer this basic question of muscle adaptation.
5. Does suppression of mir-1 and/or mir-133a lead to skeletal
muscle hypertrophy?
The observation that mir-1 and 133a levels go down following resistance
exercise presents the hypothesis that these miRNAs drive the transcriptional
changes leading to muscle hypertrophy. This can be directly tested in vitro by
knocking down the miR-1-1/133a-2 bicistronic transcript in muscle cells in
vitro and determining whether this results in an increase in myotube size.
6. How does resistance exercise alter miRNA levels?
The expression of miRNAs is controlled by specific transcription factors
binding to the flanking DNA and increasing the transcription of the miRNA.
MEF2 and MyoD sites have already been identified within the upstream
region of mir-1 and mir-133a. Of these, MyoD is known to be upregulated
following resistance exercise [323-325]. This correlates with the increase in
primir-1 and 133a following overload. However, levels of the mature mir-1
and 133a decrease suggesting that the processing of these miRNAs is
regulated following resistance exercise. The identity of the processing factor
and its control following resistance exercise is of extreme interest if mir-1
and/or mir-133a drive skeletal muscle hypertrophy.
7. How do we identify novel factors that regulate skeletal
muscle hypertrophy?
Human and animal models of resistance exercise can be used to screen
for factors by looking for changes in signalling cascades that are exercise-
specific. However, this will require large-scale investigation of differential
phosphorylation following resistance or endurance exercise using proteome-
scale screening techniques. Without such techniques, the discovery of factors
that are activated by resistance but not endurance exercise will rely on luck.

Molecular mechanisms of skeletal muscle hypertrophy
79
For instance, the discovery that S6K1 undergoes a prolonged phosphorylation
in response to resistance exercise but not endurance exercise was made while
investigating the role of 4EBP in exercise [80]. In the future, quantitative
mass spectroscopy of proteins from control, resistance exercised, and
endurance exercised muscle may well identify novel proteins that play an
important role in the adaptation to resistance training. However, these
experiments will be quite costly and will require diligent post hoc confirmation
to show that the signalling dynamics of the identified proteins are consistent
with one or more of the processes involved in hypertrophy and that when the
activity of the protein is altered this has the appropriate effect on hypertrophy.
8. Can tissue culture support the identification and
characterisation of hypertrophic factors?
Tissue culture models have been extensively used to analyse specific
signalling molecules in skeletal muscle. These models traditionally used
transient transfection to overexpress the protein of interest and determine its
effect on muscle size. One of the problems with these studies is that they use
supraphysiological levels of the protein of interest and never do the important
confirming experiments such as: (1) expressing of an inhibitor resistant
protein that rescues the effect of the inhibitor; (2) expressing a dominant
negative form of the protein that can inhibit growth; and (3) expressing
downstream targets to recapitulate the phenotype and circumvent inhibitors.
Further, care must be taken when modelling hypertrophy in tissue culture.
For example, PKB has been clearly demonstrated to play a major role in
growth factor-induced hypertrophy in culture [326], can induce muscle
growth in vivo [59], and can prevent unloading-induced atrophy [327].
However the signalling dynamics of PKB in response to resistance exercise
suggest that long-term activation of PKB is not required for muscle
hypertrophy in vivo [90,91]. This is an important point to consider when
studying transgenic animals: continuous activation of a signalling molecule
may induce growth, but this does not definitively mean that it is important in
work-induced muscle hypertrophy.
9. What constitutes in vivo verification of hypertrophic
proteins?
The wide use of knock in and knock out mice models has identified
proteins that can modulate skeletal muscle size. Indeed, if a gene is
absolutely required for muscle hypertrophy, then knocking it out or knocking
in a dominant negative form should completely block load-induced skeletal
muscle hypertrophy. One example of this was the knock in of a dominant

D. Lee Hamilton et al.
80
negative IGF receptor [71]. It was long thought that growth factors produced
locally by muscle in response to mechanical stimulation were partly or
wholly responsible for hypertrophy, however even though these animals were
growth factor resistant they still underwent normal overload-induced
hypertrophy. If a gene is proposed to prevent skeletal muscle hypertrophy then
knocking it out should relieve an inhibition. An example of this is that of the
tumour suppressor LKB1. It was hypothesised that this gene exerted a negative
effect on muscle growth by activating AMPK [210], which inhibits mTOR
[215]. However, loss of this gene had no effect on overload-induced muscle
growth in mice as a consequence of compensation from another AMPK kinase
[328]. This illustrates the importance and the drawbacks of relying on
genetically modified mice. In this example, it allowed us to identify crosstalk
between pathways. In other cases, muscle-specific knockout of a gene from the
onset of muscle creatine kinase or -skeletal actin expression, results in large-
scale cellular adaptations that can compensate for the loss of a specific gene.
These adaptations are largely non-physiological and introduce confounding
variables that prevent proper analysis of the gene of interest. Some of this
compensation can be overcome by knocking in genes with mutations in sites
of posttranslational modification such as phosphorylation, neddylation or
sumoylation rather than knocking them out and this may reveal the
physiological relevance of these sites in response to resistance exercise.
10. How can we screen for clinical potential?
When assessing the clinical potential of a pharmacological target it is
essential that the outcome measure is force production. Muscle hypertrophy
is the consequence of increased protein turnover in favour of protein
accretion. Degradation is still a component of the response so that
architectural remodelling can occur. Simply inducing growth may not be
enough to improve force output. Increasing muscle force production is the
result of adaptations within the muscle and the muscle ECM that allows force
transduction. The only way to assess if a treatment is effective is to measure
the force output of the muscle either in vivo, or in tissue culture with 3D
muscle constructs. It is important to remember that having a bigger muscle
mass without improved force generating capacity would result in greater
disability due to the greater mass that would need to be supported.
Summary
The molecular mechanism of skeletal muscle hypertrophy is a complex
interaction between translation and transcriptional events leading to
accelerated accretion of protein. Over the past 10 years, key signaling factors
that control these processes have been identified. Further, there appears to be

Molecular mechanisms of skeletal muscle hypertrophy
81
considerable crosstalk between these molecular pathways, suggesting that a
common unifying process may underlie the phenotypic changes following
resistance exercise. However, identification of this factor is beyond the
current work in this area. The search for the unifying mechanism of muscle
hypertrophy has been hampered by the fact that there are many roads that
lead to increased muscle size and the current literature does not discern
between resistance exercise-induced hypertrophy and developmental or
growth factor-induced muscle growth. While some commonality likely exists
between the pathways it is the differences that will be the key to understanding
how to pharmacologically increase muscle mass without causing growth
related diseases such as cancer. There is still a lot to learn in this field and a
great deal of therapeutic potential to be exploited. Due to the complexity of
the signaling it is likely to be some time before effective pharmacological
therapies to increase muscle mass are developed, for now the best approach is
a good regime of resistance exercise in combination with well timed nutrition.
Acknowledgements
This work was supported by grants from the Wellcome Trust (077426).
References
1. Delorme TL, W.A. 1948, Archives of Physical Medicine and Rehabilitation, 29,
263-273.
2. Lutz, W. and Qiang, R. 2002, Philos Trans R Soc Lond B Biol Sci, 357, 1197-210.
3. Scuffham, P., Chaplin, S. and Legood, R. 2003, J Epidemiol Community Health,
57, 740-4.
4. Knickman, J.R. and Snell, E.K. 2002, Health Serv Res, 37, 849-84.
5. Evans, W.J., Roubenoff, R. and Shevitz, A. 1998, Semin Oncol, 25, 112-22.
6. Tisdale, M.J. 1999, J Nutr, 129, 243S-246S.
7. Hasselgren, P.O., Menconi, M.J., Fareed, M.U., Yang, H., Wei, W. and Evenson,
A. 2005, Int J Biochem Cell Biol, 37, 2156-68.
8. Wouters, E.F., Creutzberg, E.C. and Schols, A.M. 2002, Chest, 121, 127S-130S.
9. Lecker, S.H., Solomon, V., Mitch, W.E. and Goldberg, A.L. 1999, J Nutr, 129,
227S-237S.
10. Baumgartner, R.N., Koehler, K.M., Gallagher, D., Romero, L., Heymsfield, S.B.,
Ross, R.R., Garry, P.J. and Lindeman, R.D. 1998, Am J Epidemiol, 147, 755-63.
11. Dawson D, H.G., Fulton J. 1987, Vital and health statistics of the national centre
for health statistics., 133, 87-1250.
12. Booth, F.W. and Thomason, D.B. 1991, Physiol Rev, 71, 541-85.
13. Phillips, S.M., Tipton, K.D., Aarsland, A., Wolf, S.E. and Wolfe, R.R. 1997, Am
J Physiol, 273, E99-107.
14. Biolo, G., Maggi, S.P., Williams, B.D., Tipton, K.D. and Wolfe, R.R. 1995, Am J
Physiol, 268, E514-20.

D. Lee Hamilton et al.
82
15. Chesley, A., MacDougall, J.D., Tarnopolsky, M.A., Atkinson, S.A. and Smith, K.
1992, J Appl Physiol, 73, 1383-8.
16. MacDougall, J.D., Gibala, M.J., Tarnopolsky, M.A., MacDonald, J.R., Interisano,
S.A. and Yarasheski, K.E. 1995, Can J Appl Physiol, 20, 480-6.
17. Tipton, K.D., Ferrando, A.A., Phillips, S.M., Doyle, D., Jr. and Wolfe, R.R.
1999, Am J Physiol, 276, E628-34.
18. Roy, B.D., Tarnopolsky, M.A., MacDougall, J.D., Fowles, J. and Yarasheski,
K.E. 1997, J Appl Physiol, 82, 1882-8.
19. Koopman, R., Wagenmakers, A.J., Manders, R.J., Zorenc, A.H., Senden, J.M.,
Gorselink, M., Keizer, H.A. and van Loon, L.J. 2005, Am J Physiol Endocrinol
Metab, 288, E645-53.
20. Esmarck, B., Andersen, J.L., Olsen, S., Richter, E.A., Mizuno, M. and Kjaer, M.
2001, J Physiol, 535, 301-11.
21. Luthi, J.M., Howald, H., Claassen, H., Rosler, K., Vock, P. and Hoppeler, H.
1986, Int J Sports Med, 7, 123-7.
22. Claassen, H., Gerber, C., Hoppeler, H., Luthi, J.M. and Vock, P. 1989, J Physiol,
409, 491-5.
23. Narici, M.V., Hoppeler, H., Kayser, B., Landoni, L., Claassen, H., Gavardi, C.,
Conti, M. and Cerretelli, P. 1996, Acta Physiol Scand, 157, 175-86.
24. Bouchard, C., An, P., Rice, T., Skinner, J.S., Wilmore, J.H., Gagnon, J., Perusse,
L., Leon, A.S. and Rao, D.C. 1999, J Appl Physiol, 87, 1003-8.
25. Goldberg, A.L. 1972, Muscle Biol, 1, 89-118.
26. Timson, B.F. 1990, J Appl Physiol, 69, 1935-45.
27. Lowe, D.A. and Alway, S.E. 2002, J Orthop Sports Phys Ther, 32, 36-43.
28. Thomsen, P. and Luco, J.V. 1944, Journal of Neurophysiology, 7, 246-251.
29. Sola, O.M. and Martin, A.W. 1953, Am J Physiol, 172, 324-32.
30. Sola, O.M., Christensen, D.L. and Martin, A.W. 1973, Exp Neurol, 41, 76-100.
31. Carson, J.A. 1997, Exerc Sport Sci Rev, 25, 301-20.
32. Goldberg, A.L., Etlinger, J.D., Goldspink, D.F. and Jablecki, C. 1975, Med Sci
Sports, 7, 185-98.
33. Gutmann, E., Schiaffino, S. and Hanzlikova, V. 1971, Exp Neurol, 31, 451-64.
34. Ianuzzo, C.D. and Chen, V. 1977, Physiol Teach, 6, 4-7.
35. Folland, J.P. and Williams, A.G. 2007, Sports Med, 37, 145-68.
36. Ianuzzo, C.D., Gollnick, P.D. and Armstrong, R.B. 1976, Life Sci, 19, 1517-23.
37. Gollnick, P.D., Armstrong, R.B., Saubert, C.W.t., Piehl, K. and Saltin, B. 1972, J
Appl Physiol, 33, 312-9.
38. Karlsson, J., Sjodin, B., Thorstensson, A., Hulten, B. and Frith, K. 1975, Acta
Physiol Scand, 93, 150-6.
39. Staron, R.S., Karapondo, D.L., Kraemer, W.J., Fry, A.C., Gordon, S.E., Falkel,
J.E., Hagerman, F.C. and Hikida, R.S. 1994, J Appl Physiol, 76, 1247-55.
40. Canepari, M., Rossi, R., Pellegrino, M.A., Orrell, R.W., Cobbold, M., Harridge,
S. and Bottinelli, R. 2005, J Appl Physiol, 98, 2390-5.
41. Gonyea, W.J. and Ericson, G.C. 1976, J Appl Physiol, 40, 630-3.
42. Klitgaard, H. 1988, J Appl Physiol, 64, 1740-5.

Molecular mechanisms of skeletal muscle hypertrophy
83
43. Ho, K.W., Roy, R.R., Tweedle, C.D., Heusner, W.W., Van Huss, W.D. and
Carrow, R.E. 1980, Am J Anat, 157, 433-40.
44. Wong, T.S. and Booth, F.W. 1988, J Appl Physiol, 65, 950-4.
45. Vandenburgh, H.H. 1987, Med Sci Sports Exerc, 19, S142-9.
46. Yaffe, D. and Saxel, O. 1977, Differentiation, 7, 159-66.
47. Vandenburgh, H. and Kaufman, S. 1979, Science, 203, 265-8.
48. Holly, R.G., Barnett, J.G., Ashmore, C.R., Taylor, R.G. and Mole, P.A. 1980,
Am J Phys Cell Physiol, C62-C71.
49. Goldspink, D.F. 1978, Biochem J, 174, 595-602.
50. Gordon, E.E., Kowalski, K. and Fritts, M. 1967, Jama, 199, 103-8.
51. Yarasheski, K., Aroniadou, V. and Lemon, P.W. 1987, Medicine and Science in
Sports and Exercise, 19, S15.
52. Ingber, D.E. 1997, Annu Rev Physiol, 59, 575-99.
53. Keller, R.S., Shai, S.Y., Babbitt, C.J., Pham, C.G., Solaro, R.J., Valencik, M.L.,
Loftus, J.C. and Ross, R.S. 2001, Am J Pathol, 158, 1079-90.
54. Shai, S.Y., Harpf, A.E. and Ross, R.S. 2002, Genet Eng (N Y), 24, 87-105.
55. Bendig, G., Grimmler, M., Huttner, I.G., Wessels, G., Dahme, T., Just, S., Trano,
N., Katus, H.A., Fishman, M.C. and Rottbauer, W. 2006, Genes Dev, 20, 2361-72.
56. Brancaccio, M., Fratta, L., Notte, A., Hirsch, E., Poulet, R., Guazzone, S., De
Acetis, M., Vecchione, C., Marino, G., Altruda, F., Silengo, L., Tarone, G. and
Lembo, G. 2003, Nat Med, 9, 68-75.
57. De Acetis, M., Notte, A., Accornero, F., Selvetella, G., Brancaccio, M.,
Vecchione, C., Sbroggio, M., Collino, F., Pacchioni, B., Lanfranchi, G., Aretini,
A., Ferretti, R., Maffei, A., Altruda, F., Silengo, L., Tarone, G. and Lembo, G.
2005, Circ Res, 96, 1087-94.
58. Knoll, R., Hoshijima, M., Hoffman, H.M., Person, V., Lorenzen-Schmidt, I., Bang,
M.L., Hayashi, T., Shiga, N., Yasukawa, H., Schaper, W., McKenna, W., Yokoyama,
M., Schork, N.J., Omens, J.H., McCulloch, A.D., Kimura, A., Gregorio, C.C., Poller,
W., Schaper, J., Schultheiss, H.P. and Chien, K.R. 2002, Cell, 111, 943-55.
59. Bodine, S.C., Stitt, T.N., Gonzalez, M., Kline, W.O., Stover, G.L., Bauerlein, R.,
Zlotchenko, E., Scrimgeour, A., Lawrence, J.C., Glass, D.J. and Yancopoulos,
G.D. 2001, Nat Cell Biol, 3, 1014-9.
60. Carson, J.A. and Wei, L. 2000, J Appl Physiol, 88, 337-43.
61. Fluck, M., Carson, J.A., Gordon, S.E., Ziemiecki, A. and Booth, F.W. 1999, Am
J Physiol, 277, C152-62.
62. Gordon, S.E., Fluck, M. and Booth, F.W. 2001, J Appl Physiol, 90, 1174-83;
discussion 1165.
63. Guharay, F. and Sachs, F. 1984, J Physiol, 352, 685-701.
64. Spangenburg, E.E. and McBride, T.A. 2006, J Appl Physiol, 100, 129-35.
65. Kameyama, T. and Etlinger, J.D. 1979, Nature, 279, 344-6.
66. Semsarian, C., Wu, M.J., Ju, Y.K., Marciniec, T., Yeoh, T., Allen, D.G., Harvey,
R.P. and Graham, R.M. 1999, Nature, 400, 576-81.
67. Parsons, S.A., Millay, D.P., Wilkins, B.J., Bueno, O.F., Tsika, G.L., Neilson,
J.R., Liberatore, C.M., Yutzey, K.E., Crabtree, G.R., Tsika, R.W. and Molkentin,
J.D. 2004, J Biol Chem, 279, 26192-200.

D. Lee Hamilton et al.
84
68. Florini, J.R., Ewton, D.Z. and Coolican, S.A. 1996, Endocr Rev, 17, 481-517.
69. Turner, J.D., Rotwein, P., Novakofski, J. and Bechtel, P.J. 1988, Am J Physiol,
255, E513-7.
70. McKoy, G., Ashley, W., Mander, J., Yang, S.Y., Williams, N., Russell, B. and
Goldspink, G. 1999, J Physiol, 516 (Pt 2), 583-92.
71. Spangenburg, E.E., Le Roith, D., Ward, C.W. and Bodine, S.C. 2008, J Physiol,
586, 283-91.
72. Song, Y.H., Godard, M., Li, Y., Richmond, S.R., Rosenthal, N. and Delafontaine,
P. 2005, J Investig Med, 53, 135-42.
73. Clarke, M.S. and Feeback, D.L. 1996, Faseb J, 10, 502-9.
74. Baar, K., Torgan, C.E., Kraus, W.E. and Esser, K. 2000, Mol Cell Biol Res
Commun, 4, 76-80.
75. Vandenburgh, H.H. and Kaufman, S. 1982, J Biol Chem, 257, 13448-54.
76. Vandenburgh, H.H. and Kaufman, S. 1981, J Cell Physiol, 109, 205-14.
77. Goldberg, A.L. and Goodman, H.M. 1969, Am J Physiol, 216, 1111-5.
78. Laurent, G.J. and Millward, D.J. 1980, Fed Proc, 39, 42-7.
79. Wong, T.S. and Booth, F.W. 1990, J Appl Physiol, 69, 1709-17.
80. Baar, K. and Esser, K. 1999, Am J Physiol, 276, C120-7.
81. Merrick, W.C. 1992, Microbiol Rev, 56, 291-315.
82. Hornberger, T.A., Sukhija, K.B. and Chien, S. 2006, Cell Cycle, 5, 1391-6.
83. Corradetti, M.N., Inoki, K., Bardeesy, N., DePinho, R.A. and Guan, K.L. 2004,
Genes Dev, 18, 1533-8.
84. Proud, C.G. 2006, Biochem Soc Trans, 34, 213-6.
85. Proud, C.G. 2004, Curr Top Microbiol Immunol, 279, 215-44.
86. Wang, X. and Proud, C.G. 2006, Physiology (Bethesda), 21, 362-9.
87. Hornberger, T.A. and Chien, S. 2006, J Cell Biochem, 97, 1207-16.
88. Hornberger, T.A., Stuppard, R., Conley, K.E., Fedele, M.J., Fiorotto, M.L., Chin,
E.R. and Esser, K.A. 2004, Biochem J, 380, 795-804.
89. Bolster, D.R., Kubica, N., Crozier, S.J., Williamson, D.L., Farrell, P.A., Kimball,
S.R. and Jefferson, L.S. 2003, J Physiol, 553, 213-20.
90. Nader, G.A. and Esser, K.A. 2001, J Appl Physiol, 90, 1936-42.
91. Parkington, J.D., Siebert, A.P., LeBrasseur, N.K. and Fielding, R.A. 2003, Am J
Physiol Regul Integr Comp Physiol, 285, R1086-90.
92. Coffey, V.G., Zhong, Z., Shield, A., Canny, B.J., Chibalin, A.V., Zierath, J.R.
and Hawley, J.A. 2006, Faseb J, 20, 190-2.
93. Eliasson, J., Elfegoun, T., Nilsson, J., Kohnke, R., Ekblom, B. and Blomstrand,
E. 2006, Am J Physiol Endocrinol Metab, 291, E1197-205.
94. Terzis, G., Georgiadis, G., Stratakos, G., Vogiatzis, I., Kavouras, S., Manta, P.,
Mascher, H. and Blomstrand, E. 2008, Eur J Appl Physiol, 102, 145-52.
95. Wolin, S.L. and Walter, P. 1988, Embo J, 7, 3559-69.
96. Gingras, A.C., Raught, B. and Sonenberg, N. 1999, Annu Rev Biochem, 68,
913-63.
97. Kozak, M. 1989, J Cell Biol, 108, 229-41.
98. Hershey, J.W. 1991, Annu Rev Biochem, 60, 717-55.
99. Abraham, R.T. 1996, Curr Opin Immunol, 8, 412-8.

Molecular mechanisms of skeletal muscle hypertrophy
85
100. Brunn, G.J., Fadden, P., Haystead, T.A. and Lawrence, J.C., Jr. 1997, J Biol
Chem, 272, 32547-50.
101. Kim, D.H., Sarbassov, D.D., Ali, S.M., Latek, R.R., Guntur, K.V., Erdjument-
Bromage, H., Tempst, P. and Sabatini, D.M. 2003, Mol Cell, 11, 895-904.
102. Hara, K., Maruki, Y., Long, X., Yoshino, K., Oshiro, N., Hidayat, S., Tokunaga,
C., Avruch, J. and Yonezawa, K. 2002, Cell, 110, 177-89.
103. Sarbassov, D.D., Ali, S.M., Kim, D.H., Guertin, D.A., Latek, R.R., Erdjument-
Bromage, H., Tempst, P. and Sabatini, D.M. 2004, Curr Biol, 14, 1296-302.
104. Yang, Q., Inoki, K., Ikenoue, T. and Guan, K.L. 2006, Genes Dev, 20, 2820-32.
105. Pearce, L.R., Huang, X., Boudeau, J., Pawlowski, R., Wullschleger, S., Deak, M.,
Ibrahim, A.F., Gourlay, R., Magnuson, M.A. and Alessi, D.R. 2007, Biochem J,
405, 513-22.
106. Woo, S.Y., Kim, D.H., Jun, C.B., Kim, Y.M., Haar, E.V., Lee, S.I., Hegg, J.W.,
Bandhakavi, S., Griffin, T.J. and Kim, D.H. 2007, J Biol Chem, 282, 25604-12.
107. Guertin, D.A., Stevens, D.M., Thoreen, C.C., Burds, A.A., Kalaany, N.Y.,
Moffat, J., Brown, M., Fitzgerald, K.J. and Sabatini, D.M. 2006, Dev Cell, 11,
859-71.
108. Schalm, S.S., Fingar, D.C., Sabatini, D.M. and Blenis, J. 2003, Curr Biol, 13,
797-806.
109. Schalm, S.S. and Blenis, J. 2002, Curr Biol, 12, 632-9.
110. Jacinto, E., Loewith, R., Schmidt, A., Lin, S., Ruegg, M.A., Hall, A. and Hall,
M.N. 2004, Nat Cell Biol, 6, 1122-8.
111. Sarbassov, D.D., Guertin, D.A., Ali, S.M. and Sabatini, D.M. 2005, Science, 307,
1098-101.
112. Heitman, J., Koller, A., Kunz, J., Henriquez, R., Schmidt, A., Movva, N.R. and
Hall, M.N. 1993, Mol Cell Biol, 13, 5010-9.
113. Guertin, D.A., Guntur, K.V., Bell, G.W., Thoreen, C.C. and Sabatini, D.M. 2006,
Curr Biol, 16, 958-70.
114. Fingar, D.C., Richardson, C.J., Tee, A.R., Cheatham, L., Tsou, C. and Blenis, J.
2004, Mol Cell Biol, 24, 200-16.
115. Fingar, D.C., Salama, S., Tsou, C., Harlow, E. and Blenis, J. 2002, Genes Dev,
16, 1472-87.
116. Beretta, L., Gingras, A.C., Svitkin, Y.V., Hall, M.N. and Sonenberg, N. 1996,
Embo J, 15, 658-64.
117. Terada, N., Patel, H.R., Takase, K., Kohno, K., Nairn, A.C. and Gelfand, E.W.
1994, Proc Natl Acad Sci U S A, 91, 11477-81.
118. Cardenas, M.E., Cutler, N.S., Lorenz, M.C., Di Como, C.J. and Heitman, J. 1999,
Genes Dev, 13, 3271-9.
119. Mahajan, P.B. 1994, Int J Immunopharmacol, 16, 711-21.
120. Mayer, C. and Grummt, I. 2006, Oncogene, 25, 6384-91.
121. Mora, A., Komander, D., van Aalten, D.M. and Alessi, D.R. 2004, Semin Cell
Dev Biol, 15, 161-70.
122. Brown, E.J., Beal, P.A., Keith, C.T., Chen, J., Shin, T.B. and Schreiber, S.L.
1995, Nature, 377, 441-6.

D. Lee Hamilton et al.
86
123. Collins, B.J., Deak, M., Arthur, J.S., Armit, L.J. and Alessi, D.R. 2003, Embo J,
22, 4202-11.
124. Lawlor, M.A., Mora, A., Ashby, P.R., Williams, M.R., Murray-Tait, V., Malone,
L., Prescott, A.R., Lucocq, J.M. and Alessi, D.R. 2002, Embo J, 21, 3728-38.
125. Schalm, S.S., Tee, A.R. and Blenis, J. 2005, J Biol Chem, 280, 11101-6.
126. Grove, J.R., Banerjee, P., Balasubramanyam, A., Coffer, P.J., Price, D.J.,
Avruch, J. and Woodgett, J.R. 1991, Mol Cell Biol, 11, 5541-50.
127. Coffer, P.J. and Woodgett, J.R. 1994, Biochem Biophys Res Commun, 198, 780-6.
128. Shima, H., Pende, M., Chen, Y., Fumagalli, S., Thomas, G. and Kozma, S.C.
1998, Embo J, 17, 6649-59.
129. Montagne, J., Stewart, M.J., Stocker, H., Hafen, E., Kozma, S.C. and Thomas, G.
1999, Science, 285, 2126-9.
130. Ohanna, M., Sobering, A.K., Lapointe, T., Lorenzo, L., Praud, C., Petroulakis, E.,
Sonenberg, N., Kelly, P.A., Sotiropoulos, A. and Pende, M. 2005, Nat Cell Biol,
7, 286-94.
131. Park, I.H., Erbay, E., Nuzzi, P. and Chen, J. 2005, Exp Cell Res, 309, 211-9.
132. Wang, X., Li, W., Williams, M., Terada, N., Alessi, D.R. and Proud, C.G. 2001,
Embo J, 20, 4370-9.
133. Ryazanov, A.G., Shestakova, E.A. and Natapov, P.G. 1988, Nature, 334, 170-3.
134. Redpath, N.T. and Proud, C.G. 1994, Biochim Biophys Acta, 1220, 147-62.
135. Proud, C.G. and Denton, R.M. 1997, Biochem J, 328 (Pt 2), 329-41.
136. Holz, M.K., Ballif, B.A., Gygi, S.P. and Blenis, J. 2005, Cell, 123, 569-80.
137. Raught, B., Peiretti, F., Gingras, A.C., Livingstone, M., Shahbazian, D., Mayeur,
G.L., Polakiewicz, R.D., Sonenberg, N. and Hershey, J.W. 2004, Embo J, 23, 1761-9.
138. Rogers, G.W., Jr., Richter, N.J., Lima, W.F. and Merrick, W.C. 2001, J Biol
Chem, 276, 30914-22.
139. Abramson, R.D., Dever, T.E., Lawson, T.G., Ray, B.K., Thach, R.E. and
Merrick, W.C. 1987, J Biol Chem, 262, 3826-32.
140. Abramson, R.D., Browning, K.S., Dever, T.E., Lawson, T.G., Thach, R.E.,
Ravel, J.M. and Merrick, W.C. 1988, J Biol Chem, 263, 5462-7.
141. Thomas, G. and Hall, M.N. 1997, Curr Opin Cell Biol, 9, 782-7.
142. Meyuhas, O. 2000, Eur J Biochem, 267, 6321-30.
143. Jefferies, H.B., Thomas, G. and Thomas, G. 1994, J Biol Chem, 269, 4367-72.
144. Jefferies, H.B., Fumagalli, S., Dennis, P.B., Reinhard, C., Pearson, R.B. and
Thomas, G. 1997, Embo J, 16, 3693-704.
145. Ruvinsky, I., Sharon, N., Lerer, T., Cohen, H., Stolovich-Rain, M., Nir, T., Dor,
Y., Zisman, P. and Meyuhas, O. 2005, Genes Dev, 19, 2199-211.
146. Stolovich, M., Tang, H., Hornstein, E., Levy, G., Cohen, R., Bae, S.S., Birnbaum,
M.J. and Meyuhas, O. 2002, Mol Cell Biol, 22, 8101-13.
147. Tang, H., Hornstein, E., Stolovich, M., Levy, G., Livingstone, M., Templeton,
D., Avruch, J. and Meyuhas, O. 2001, Mol Cell Biol, 21, 8671-83.
148. Pende, M., Um, S.H., Mieulet, V., Sticker, M., Goss, V.L., Mestan, J., Mueller,
M., Fumagalli, S., Kozma, S.C. and Thomas, G. 2004, Mol Cell Biol, 24,
3112-24.

Molecular mechanisms of skeletal muscle hypertrophy
87
149. Hannan, K.M., Brandenburger, Y., Jenkins, A., Sharkey, K., Cavanaugh, A.,
Rothblum, L., Moss, T., Poortinga, G., McArthur, G.A., Pearson, R.B. and
Hannan, R.D. 2003, Mol Cell Biol, 23, 8862-77.
150. Zhang, C., Comai, L. and Johnson, D.L. 2005, Mol Cell Biol, 25, 6899-911.
151. Russell, J. and Zomerdijk, J.C. 2005, Trends Biochem Sci, 30, 87-96.
152. Richardson, C.J., Broenstrup, M., Fingar, D.C., Julich, K., Ballif, B.A., Gygi, S.
and Blenis, J. 2004, Curr Biol, 14, 1540-9.
153. Zhou, Z., Luo, M.J., Straesser, K., Katahira, J., Hurt, E. and Reed, R. 2000,
Nature, 407, 401-5.
154. Lazaris-Karatzas, A., Montine, K.S. and Sonenberg, N. 1990, Nature, 345, 544-7.
155. Lazaris-Karatzas, A. and Sonenberg, N. 1992, Mol Cell Biol, 12, 1234-8.
156. De Benedetti, A. and Rhoads, R.E. 1990, Proc Natl Acad Sci U S A, 87, 8212-6.
157. Graff, J.R. and Zimmer, S.G. 2003, Clin Exp Metastasis, 20, 265-73.
158. De Benedetti, A., Joshi-Barve, S., Rinker-Schaeffer, C. and Rhoads, R.E. 1991,
Mol Cell Biol, 11, 5435-45.
159. Shantz, L.M. and Pegg, A.E. 1994, Cancer Res, 54, 2313-6.
160. Kevil, C.G., De Benedetti, A., Payne, D.K., Coe, L.L., Laroux, F.S. and
Alexander, J.S. 1996, Int J Cancer, 65, 785-90.
161. Rosenwald, I.B., Kaspar, R., Rousseau, D., Gehrke, L., Leboulch, P., Chen, J.J.,
Schmidt, E.V., Sonenberg, N. and London, I.M. 1995, J Biol Chem, 270, 21176-80.
162. Rosenwald, I.B., Lazaris-Karatzas, A., Sonenberg, N. and Schmidt, E.V. 1993,
Mol Cell Biol, 13, 7358-63.
163. Lachance, P.E., Miron, M., Raught, B., Sonenberg, N. and Lasko, P. 2002, Mol
Cell Biol, 22, 1656-63.
164. Pause, A., Belsham, G.J., Gingras, A.C., Donze, O., Lin, T.A., Lawrence, J.C., Jr.
and Sonenberg, N. 1994, Nature, 371, 762-7.
165. Lin, T.A., Kong, X., Saltiel, A.R., Blackshear, P.J. and Lawrence, J.C., Jr. 1995,
J Biol Chem, 270, 18531-8.
166. Lin, T.A. and Lawrence, J.C., Jr. 1996, J Biol Chem, 271, 30199-204.
167. Diggle, T.A., Moule, S.K., Avison, M.B., Flynn, A., Foulstone, E.J., Proud, C.G.
and Denton, R.M. 1996, Biochem J, 316 (Pt 2), 447-53.
168. von Manteuffel, S.R., Gingras, A.C., Ming, X.F., Sonenberg, N. and Thomas, G.
1996, Proc Natl Acad Sci U S A, 93, 4076-80.
169. Hara, K., Yonezawa, K., Kozlowski, M.T., Sugimoto, T., Andrabi, K., Weng,
Q.P., Kasuga, M., Nishimoto, I. and Avruch, J. 1997, J Biol Chem, 272, 26457-63.
170. Azpiazu, I., Saltiel, A.R., DePaoli-Roach, A.A. and Lawrence, J.C. 1996, J Biol
Chem, 271, 5033-9.
171. Dostie, J., Ferraiuolo, M., Pause, A., Adam, S.A. and Sonenberg, N. 2000, Embo
J, 19, 3142-56.
172. Cohen, N., Sharma, M., Kentsis, A., Perez, J.M., Strudwick, S. and Borden, K.L.
2001, Embo J, 20, 4547-59.
173. Nader, G.A., McLoughlin, T.J. and Esser, K.A. 2005, Am J Physiol Cell Physiol,
289, C1457-65.
174. Blackshear, P.J., Stumpo, D.J., Carballo, E. and Lawrence, J.C., Jr. 1997, J Biol
Chem, 272, 31510-4.

D. Lee Hamilton et al.
88
175. Le Bacquer, O., Petroulakis, E., Paglialunga, S., Poulin, F., Richard, D.,
Cianflone, K. and Sonenberg, N. 2007, J Clin Invest, 117, 387-96.
176. Kubica, N., Bolster, D.R., Farrell, P.A., Kimball, S.R. and Jefferson, L.S. 2005, J
Biol Chem, 280, 7570-80.
177. Koopman, R., Zorenc, A.H., Gransier, R.J., Cameron-Smith, D. and van Loon,
L.J. 2006, Am J Physiol Endocrinol Metab, 290, E1245-52.
178. Farrell, P.A., Hernandez, J.M., Fedele, M.J., Vary, T.C., Kimball, S.R. and
Jefferson, L.S. 2000, J Appl Physiol, 88, 1036-42.
179. Bolster, D.R., Kimball, S.R. and Jefferson, L.S. 2003, Exerc Sport Sci Rev, 31,
111-6.
180. Pedersen, B.K., Akerstrom, T.C., Nielsen, A.R. and Fischer, C.P. 2007, J Appl
Physiol, 103, 1093-8.
181. Nielsen, A.R. and Pedersen, B.K. 2007, Appl Physiol Nutr Metab, 32, 833-9.
182. Serrano, A.L., Baeza-Raja, B., Perdiguero, E., Jardi, M. and Munoz-Canoves, P.
2008, Cell Metab, 7, 33-44.
183. Charge, S. and Rudnicki, M.A. 2003, Cell, 113, 422-3.
184. Busquets, S., Figueras, M.T., Meijsing, S., Carbo, N., Quinn, L.S., Almendro, V.,
Argiles, J.M. and Lopez-Soriano, F.J. 2005, Int J Mol Med, 16, 471-6.
185. Furmanczyk, P.S. and Quinn, L.S. 2003, Cell Biol Int, 27, 845-51.
186. Quinn, L.S., Anderson, B.G., Drivdahl, R.H., Alvarez, B. and Argiles, J.M. 2002,
Exp Cell Res, 280, 55-63.
187. Quinn, L.S., Haugk, K.L. and Grabstein, K.H. 1995, Endocrinology, 136, 3669-72.
188. Nielsen, A.R., Mounier, R., Plomgaard, P., Mortensen, O.H., Penkowa, M.,
Speerschneider, T., Pilegaard, H. and Pedersen, B.K. 2007, J Physiol, 584, 305-12.
189. Grzelkowska, K., Dardevet, D., Balage, M. and Grizard, J. 1999, J Endocrinol,
160, 137-45.
190. Rommel, C., Bodine, S.C., Clarke, B.A., Rossman, R., Nunez, L., Stitt, T.N.,
Yancopoulos, G.D. and Glass, D.J. 2001, Nat Cell Biol, 3, 1009-13.
191. Kline, W.O., Panaro, F.J., Yang, H. and Bodine, S.C. 2007, J Appl Physiol, 102,
740-7.
192. Inoki, K., Ouyang, H., Zhu, T., Lindvall, C., Wang, Y., Zhang, X., Yang, Q.,
Bennett, C., Harada, Y., Stankunas, K., Wang, C.Y., He, X., MacDougald, O.A.,
You, M., Williams, B.O. and Guan, K.L. 2006, Cell, 126, 955-68.
193. Czech, M.P. 1985, Ann. Rev. Physiol, 47, 357-81.
194. Rechler, M.M. and Nissley, S.P. 1985, Ann. Rev. Physiol, 47, 425-42.
195. Ceresa, B.P. and Pessin, J.E. 1998, Mol Cell Biochem, 182, 23-9.
196. White, M.F. 1998, Mol Cell Biochem, 182, 3-11.
197. Kapeller, R. and Cantley, L.C. 1994, Bioessays, 16, 565-76.
198. Vanhaesebroeck, B. and Alessi, D.R. 2000, Biochem J, 346 Pt 3, 561-76.
199. Scott, P.H., Brunn, G.J., Kohn, A.D., Roth, R.A. and Lawrence, J.C., Jr. 1998,
Proc Natl Acad Sci U S A, 95, 7772-7.
200. Nave, B.T., Ouwens, M., Withers, D.J., Alessi, D.R. and Shepherd, P.R. 1999,
Biochem J, 344 Pt 2, 427-31.
201. Inoki, K., Li, Y., Zhu, T., Wu, J. and Guan, K.L. 2002, Nat Cell Biol, 4, 648-57.

Molecular mechanisms of skeletal muscle hypertrophy
89
202. Tee, A.R., Manning, B.D., Roux, P.P., Cantley, L.C. and Blenis, J. 2003, Curr
Biol, 13, 1259-68.
203. Garami, A., Zwartkruis, F.J., Nobukuni, T., Joaquin, M., Roccio, M., Stocker, H.,
Kozma, S.C., Hafen, E., Bos, J.L. and Thomas, G. 2003, Mol Cell, 11, 1457-66.
204. Inoki, K., Li, Y., Xu, T. and Guan, K.L. 2003, Genes Dev, 17, 1829-34.
205. Roux, P.P., Ballif, B.A., Anjum, R., Gygi, S.P. and Blenis, J. 2004, Proc Natl
Acad Sci U S A, 101, 13489-94.
206. Hickson, R.C. 1980, Eur J Appl Physiol Occup Physiol, 45, 255-63.
207. Nader, G.A. 2006, Med Sci Sports Exerc, 38, 1965-70.
208. Hawley, S.A., Selbert, M.A., Goldstein, E.G., Edelman, A.M., Carling, D. and
Hardie, D.G. 1995, J Biol Chem, 270, 27186-91.
209. Sakamoto, K., McCarthy, A., Smith, D., Green, K.A., Grahame Hardie, D.,
Ashworth, A. and Alessi, D.R. 2005, Embo J, 24, 1810-20.
210. Hawley, S.A., Boudeau, J., Reid, J.L., Mustard, K.J., Udd, L., Makela, T.P.,
Alessi, D.R. and Hardie, D.G. 2003, J Biol, 2, 28.
211. Hawley, S.A., Pan, D.A., Mustard, K.J., Ross, L., Bain, J., Edelman, A.M.,
Frenguelli, B.G. and Hardie, D.G. 2005, Cell Metab, 2, 9-19.
212. Woods, A., Johnstone, S.R., Dickerson, K., Leiper, F.C., Fryer, L.G., Neumann,
D., Schlattner, U., Wallimann, T., Carlson, M. and Carling, D. 2003, Curr Biol,
13, 2004-8.
213. Towler, M.C. and Hardie, D.G. 2007, Circ Res, 100, 328-41.
214. Bolster, D.R., Crozier, S.J., Kimball, S.R. and Jefferson, L.S. 2002, J Biol Chem,
277, 23977-80.
215. Inoki, K., Zhu, T. and Guan, K.L. 2003, Cell, 115, 577-90.
216. Winder, W.W. and Hardie, D.G. 1996, Am J Physiol, 270, E299-304.
217. Winder, W.W., Taylor, E.B. and Thomson, D.M. 2006, Med Sci Sports Exerc,
38, 1945-9.
218. Tinton, S.A. and Buc-Calderon, P.M. 1999, FEBS Lett, 446, 55-9.
219. Sofer, A., Lei, K., Johannessen, C.M. and Ellisen, L.W. 2005, Mol Cell Biol, 25,
5834-45.
220. DeYoung, M.P., Horak, P., Sofer, A., Sgroi, D. and Ellisen, L.W. 2008, Genes
Dev, 22, 239-51.
221. Kimball, S.R., Do, A.N., Kutzler, L., Cavener, D.R. and Jefferson, L.S. 2008, J
Biol Chem, 283, 3465-75.
222. Kawada, S. and Ishii, N. 2005, Med Sci Sports Exerc, 37, 1144-50.
223. Abe, T., Kearns, C.F. and Sato, Y. 2006, J Appl Physiol, 100, 1460-6.
224. Fujita, S., Abe, T., Drummond, M.J., Cadenas, J.G., Dreyer, H.C., Sato, Y.,
Volpi, E. and Rasmussen, B.B. 2007, J Appl Physiol, 103, 903-10.
225. Phillips, S.M., Tipton, K.D., Ferrando, A.A. and Wolfe, R.R. 1999, Am J
Physiol, 276, E118-24.
226. Tipton, K.D. and Wolfe, R.R. 1998, Acta Physiol Scand, 162, 377-87.
227. Tipton, K.D. and Wolfe, R.R. 2001, Int J Sport Nutr Exerc Metab, 11, 109-32.
228. Andersen, L.L., Tufekovic, G., Zebis, M.K., Crameri, R.M., Verlaan, G., Kjaer,
M., Suetta, C., Magnusson, P. and Aagaard, P. 2005, Metabolism, 54, 151-6.

D. Lee Hamilton et al.
90
229. Meijer, A.J. and Dubbelhuis, P.F. 2004, Biochem Biophys Res Commun, 313,
397-403.
230. Anthony, J.C., Yoshizawa, F., Anthony, T.G., Vary, T.C., Jefferson, L.S. and
Kimball, S.R. 2000, J Nutr, 130, 2413-9.
231. Yoshizawa, F., Kimball, S.R. and Jefferson, L.S. 1997, Biochem Biophys Res
Commun, 240, 825-31.
232. Yoshizawa, F., Kimball, S.R., Vary, T.C. and Jefferson, L.S. 1998, Am J Physiol,
275, E814-20.
233. Crozier, S.J., Kimball, S.R., Emmert, S.W., Anthony, J.C. and Jefferson, L.S.
2005, J Nutr, 135, 376-82.
234. Proud, C.G. 2002, Eur J Biochem, 269, 5338-49.
235. Byfield, M.P., Murray, J.T. and Backer, J.M. 2005, J Biol Chem, 280, 33076-82.
236. Eskelinen, E.L., Prescott, A.R., Cooper, J., Brachmann, S.M., Wang, L., Tang,
X., Backer, J.M. and Lucocq, J.M. 2002, Traffic, 3, 878-93.
237. Petiot, A., Ogier-Denis, E., Blommaart, E.F., Meijer, A.J. and Codogno, P. 2000,
J Biol Chem, 275, 992-8.
238. Findlay, G.M., Yan, L., Procter, J., Mieulet, V. and Lamb, R.F. 2007, Biochem J,
403, 13-20.
239. Tipton, K.D. and Witard, O.C. 2007, Clin Sports Med, 26, 17-36.
240. Atherton, P.J., Babraj, J., Smith, K., Singh, J., Rennie, M.J. and Wackerhage, H.
2005, Faseb J, 19, 786-8.
241. Sakamoto, K., Hirshman, M.F., Aschenbach, W.G. and Goodyear, L.J. 2002, J
Biol Chem, 277, 11910-7.
242. Hornberger, T.A., Chu, W.K., Mak, Y.W., Hsiung, J.W., Huang, S.A. and Chien,
S. 2006, Proc Natl Acad Sci U S A, 103, 4741-6.
243. Cleland, P.J., Appleby, G.J., Rattigan, S. and Clark, M.G. 1989, J Biol Chem,
264, 17704-11.
244. Dhalla, N.S., Xu, Y.J., Sheu, S.S., Tappia, P.S. and Panagia, V. 1997, J Mol Cell
Cardiol, 29, 2865-71.
245. Fang, Y., Vilella-Bach, M., Bachmann, R., Flanigan, A. and Chen, J. 2001,
Science, 294, 1942-5.
246. Luo, B., Prescott, S.M. and Topham, M.K. 2004, Cell Signal, 16, 891-7.
247. Harden, T.K. and Sondek, J. 2006, Annu Rev Pharmacol Toxicol, 46, 355-79.
248. Gaits, F., Fourcade, O., Le Balle, F., Gueguen, G., Gaige, B., Gassama-Diagne,
A., Fauvel, J., Salles, J.P., Mauco, G., Simon, M.F. and Chap, H. 1997, FEBS
Lett, 410, 54-8.
249. Pyne, S., Long, J.S., Ktistakis, N.T. and Pyne, N.J. 2005, Biochem Soc Trans, 33,
1370-4.
250. Tremblay, F. and Marette, A. 2001, J Biol Chem, 276, 38052-60.
251. Radimerski, T., Montagne, J., Hemmings-Mieszczak, M. and Thomas, G. 2002,
Genes Dev, 16, 2627-32.
252. Harrington, L.S., Findlay, G.M., Gray, A., Tolkacheva, T., Wigfield, S., Rebholz,
H., Barnett, J., Leslie, N.R., Cheng, S., Shepherd, P.R., Gout, I., Downes, C.P.
and Lamb, R.F. 2004, J Cell Biol, 166, 213-23.
253. Tzatsos, A. and Kandror, K.V. 2006, Mol Cell Biol, 26, 63-76.

Molecular mechanisms of skeletal muscle hypertrophy
91
254. Young, J.C., Kandarian, S.C. and Kurowski, T.G. 1992, Life Sci, 50, 1319-25.
255. Cheng, Y.J., Gregg, E.W., De Rekeneire, N., Williams, D.E., Imperatore, G.,
Caspersen, C.J. and Kahn, H.S. 2007, Diabetes Care, 30, 2264-70.
256. Holten, M.K., Zacho, M., Gaster, M., Juel, C., Wojtaszewski, J.F. and Dela, F.
2004, Diabetes, 53, 294-305.
257. Howlett, K.F., Sakamoto, K., Garnham, A., Cameron-Smith, D. and Hargreaves,
M. 2007, Diabetes, 56, 1608-14.
258. Koopman, R., Manders, R.J., Zorenc, A.H., Hul, G.B., Kuipers, H., Keizer, H.A.
and van Loon, L.J. 2005, Eur J Appl Physiol, 94, 180-7.
259. Spurlock, D.M., McDaneld, T.G. and McIntyre, L.M. 2006, BMC Genomics,
7, 320.
260. Carson, J.A., Nettleton, D. and Reecy, J.M. 2002, Faseb J, 16, 207-9.
261. Chen, Y.W., Nader, G.A., Baar, K.R., Fedele, M.J., Hoffman, E.P. and Esser,
K.A. 2002, J Physiol, 545, 27-41.
262. Kostek, M.C., Chen, Y.W., Cuthbertson, D.J., Shi, R., Fedele, M.J., Esser, K.A.
and Rennie, M.J. 2007, Physiol Genomics, 31, 42-52.
263. Jozsi, A.C., Dupont-Versteegden, E.E., Taylor-Jones, J.M., Evans, W.J., Trappe,
T.A., Campbell, W.W. and Peterson, C.A. 2000, Mech Ageing Dev, 120, 45-56.
264. Chen, Y.W., Hubal, M.J., Hoffman, E.P., Thompson, P.D. and Clarkson, P.M.
2003, J Appl Physiol, 95, 2485-94.
265. Jones, S.W., Hill, R.J., Krasney, P.A., O'Conner, B., Peirce, N. and Greenhaff,
P.L. 2004, Faseb J, 18, 1025-7.
266. Farrance, I.K. and Ordahl, C.P. 1996, J Biol Chem, 271, 8266-74.
267. Carson, J.A. and Booth, F.W. 1998, Pflugers Arch, 435, 850-8.
268. Adams, G.R., Haddad, F. and Baldwin, K.M. 1999, J Appl Physiol, 87, 1705-12.
269. Lowe, D.A. and Alway, S.E. 1999, Cell Tissue Res, 296, 531-9.
270. Carson, J.A., Schwartz, R.J. and Booth, F.W. 1996, Am J Physiol, 270, C1624-33.
271. Carson, J.A., Yan, Z., Booth, F.W., Coleman, M.E., Schwartz, R.J. and Stump,
C.S. 1995, Am J Physiol, 268, C918-24.
272. McCarthy, J.J., Fox, A.M., Tsika, G.L., Gao, L. and Tsika, R.W. 1997, Am J
Physiol, 272, R1552-61.
273. Vyas, D.R., McCarthy, J.J. and Tsika, R.W. 1999, J Biol Chem, 274, 30832-42.
274. Karasseva, N., Tsika, G., Ji, J., Zhang, A., Mao, X. and Tsika, R. 2003, Mol Cell
Biol, 23, 5143-64.
275. Thomas, M., Langley, B., Berry, C., Sharma, M., Kirk, S., Bass, J. and
Kambadur, R. 2000, J Biol Chem, 275, 40235-43.
276. Langley, B., Thomas, M., Bishop, A., Sharma, M., Gilmour, S. and Kambadur,
R. 2002, J Biol Chem, 277, 49831-40.
277. Joulia, D., Bernardi, H., Garandel, V., Rabenoelina, F., Vernus, B. and Cabello,
G. 2003, Exp Cell Res, 286, 263-75.
278. Taylor, W.E., Bhasin, S., Artaza, J., Byhower, F., Azam, M., Willard, D.H., Jr.,
Kull, F.C., Jr. and Gonzalez-Cadavid, N. 2001, Am J Physiol Endocrinol Metab,
280, E221-8.
279. McPherron, A.C. and Lee, S.J. 1997, Proc Natl Acad Sci U S A, 94, 12457-61.
280. McPherron, A.C., Lawler, A.M. and Lee, S.J. 1997, Nature, 387, 83-90.

D. Lee Hamilton et al.
92
281. Schuelke, M., Wagner, K.R., Stolz, L.E., Hubner, C., Riebel, T., Komen, W.,
Braun, T., Tobin, J.F. and Lee, S.J. 2004, N Engl J Med, 350, 2682-8.
282. Lee, S.J. 2004, Annu Rev Cell Dev Biol, 20, 61-86.
283. Massague, J. 1998, Annu Rev Biochem, 67, 753-91.
284. Tsuchida, K., Nakatani, M., Uezumi, A., Murakami, T. and Cui, X. 2008, Endocr
J, 55, 11-21.
285. Lee, S.J. and McPherron, A.C. 2001, Proc Natl Acad Sci U S A, 98, 9306-11.
286. Sutrave, P., Kelly, A.M. and Hughes, S.H. 1990, Genes Dev, 4, 1462-72.
287. Kim, J.S., Cross, J.M. and Bamman, M.M. 2005, Am J Physiol Endocrinol
Metab, 288, E1110-9.
288. Hulmi, J.J., Ahtiainen, J.P., Kaasalainen, T., Pollanen, E., Hakkinen, K., Alen,
M., Selanne, H., Kovanen, V. and Mero, A.A. 2007, Med Sci Sports Exerc, 39,
289-97.
289. Yamaguchi, A., Fujikawa, T., Shimada, S., Kanbayashi, I., Tateoka, M., Soya,
H., Takeda, H., Morita, I., Matsubara, K. and Hirai, T. 2006, Pflugers Arch, 453,
203-10.
290. Kim, J.S., Petrella, J.K., Cross, J.M. and Bamman, M.M. 2007, J Appl Physiol,
103, 1488-95.
291. Daugherty, R.L. and Gottardi, C.J. 2007, Physiology (Bethesda), 22, 303-9.
292. Petropoulos, H. and Skerjanc, I.S. 2002, J Biol Chem, 277, 15393-9.
293. Kaufmann, U., Kirsch, J., Irintchev, A., Wernig, A. and Starzinski-Powitz, A.
1999, J Cell Sci, 112 (Pt 1), 55-68.
294. Wrobel, E., Brzoska, E. and Moraczewski, J. 2007, Eur J Cell Biol, 86, 99-109.
295. Arce, L., Yokoyama, N.N. and Waterman, M.L. 2006, Oncogene, 25, 7492-504.
296. Waltzer, L. and Bienz, M. 1999, Cancer Metastasis Rev, 18, 231-46.
297. Kemler, R. 1993, Trends Genet, 9, 317-21.
298. Nastasi, T., Bongiovanni, A., Campos, Y., Mann, L., Toy, J.N., Bostrom, J.,
Rottier, R., Hahn, C., Conaway, J.W., Harris, A.J. and D'Azzo, A. 2004, Dev
Cell, 6, 269-82.
299. Baek, S.H., Kioussi, C., Briata, P., Wang, D., Nguyen, H.D., Ohgi, K.A., Glass,
C.K., Wynshaw-Boris, A., Rose, D.W. and Rosenfeld, M.G. 2003, Proc Natl
Acad Sci U S A, 100, 3245-50.
300. Shtutman, M., Zhurinsky, J., Simcha, I., Albanese, C., D'Amico, M., Pestell, R.
and Ben-Ze'ev, A. 1999, Proc Natl Acad Sci U S A, 96, 5522-7.
301. He, T.C., Sparks, A.B., Rago, C., Hermeking, H., Zawel, L., da Costa, L.T.,
Morin, P.J., Vogelstein, B. and Kinzler, K.W. 1998, Science, 281, 1509-12.
302. Xiao, G., Mao, S., Baumgarten, G., Serrano, J., Jordan, M.C., Roos, K.P.,
Fishbein, M.C. and MacLellan, W.R. 2001, Circ Res, 89, 1122-9.
303. Aschenbach, W.G., Ho, R.C., Sakamoto, K., Fujii, N., Li, Y., Kim, Y.B.,
Hirshman, M.F. and Goodyear, L.J. 2006, Am J Physiol Endocrinol Metab, 291,
E152-8.
304. Sakamoto, K., Arnolds, D.E., Ekberg, I., Thorell, A. and Goodyear, L.J. 2004,
Biochem Biophys Res Commun, 319, 419-25.
305. Ishido, M., Uda, M., Masuhara, M. and Kami, K. 2006, Acta Physiol (Oxf), 187,
407-18.

Molecular mechanisms of skeletal muscle hypertrophy
93
306. Armstrong, D.D. and Esser, K.A. 2005, Am J Physiol Cell Physiol, 289, C853-9.
307. Armstrong, D.D., Wong, V.L. and Esser, K.A. 2006, Am J Physiol Cell Physiol,
291, C185-8.
308. Jozwiak, J., Kotulska, K., Grajkowska, W., Jozwiak, S., Zalewski, W., Oldak,
M., Lojek, M., Rainko, K., Maksym, R., Lazarczyk, M., Skopinski, P. and
Wlodarski, P. 2007, Brain Dev, 29, 273-80.
309. Du, T. and Zamore, P.D. 2005, Development, 132, 4645-52.
310. Griffiths-Jones, S., Grocock, R.J., van Dongen, S., Bateman, A. and Enright, A.J.
2006, Nucleic Acids Res, 34, D140-4.
311. Bentwich, I., Avniel, A., Karov, Y., Aharonov, R., Gilad, S., Barad, O., Barzilai,
A., Einat, P., Einav, U., Meiri, E., Sharon, E., Spector, Y. and Bentwich, Z. 2005,
Nat Genet, 37, 766-70.
312. Lewis, B.P., Burge, C.B. and Bartel, D.P. 2005, Cell, 120, 15-20.
313. Callis, T.E., Chen, J.F. and Wang, D.Z. 2007, DNA Cell Biol, 26, 219-25.
314. Lim, L.P., Lau, N.C., Garrett-Engele, P., Grimson, A., Schelter, J.M., Castle, J.,
Bartel, D.P., Linsley, P.S. and Johnson, J.M. 2005, Nature, 433, 769-73.
315. Chen, J.F., Mandel, E.M., Thomson, J.M., Wu, Q., Callis, T.E., Hammond, S.M.,
Conlon, F.L. and Wang, D.Z. 2006, Nat Genet, 38, 228-33.
316. Kim, H.K., Lee, Y.S., Sivaprasad, U., Malhotra, A. and Dutta, A. 2006, J Cell
Biol, 174, 677-87.
317. Naguibneva, I., Ameyar-Zazoua, M., Polesskaya, A., Ait-Si-Ali, S., Groisman,
R., Souidi, M., Cuvellier, S. and Harel-Bellan, A. 2006, Nat Cell Biol, 8, 278-84.
318. Anderson, C., Catoe, H. and Werner, R. 2006, Nucleic Acids Res, 34, 5863-71.
319. Liu, N., Williams, A.H., Kim, Y., McAnally, J., Bezprozvannaya, S., Sutherland,
L.B., Richardson, J.A., Bassel-Duby, R. and Olson, E.N. 2007, Proc Natl Acad
Sci U S A, 104, 20844-9.
320. McCarthy, J.J. and Esser, K.A. 2007, J Appl Physiol, 102, 306-13.
321. Fluck, M., Carson, J.A., Schwartz, R.J. and Booth, F.W. 1999, J Appl Physiol,
86, 1793-9.
322. Marshall, A., Salerno, M.S., Thomas, M., Davies, T., Berry, C., Dyer, K.,
Bracegirdle, J., Watson, T., Dziadek, M., Kambadur, R., Bower, R. and Sharma,
M. 2008, Exp Cell Res, 314, 1013-29.
323. Bickel, C.S., Slade, J., Mahoney, E., Haddad, F., Dudley, G.A. and Adams, G.R.
2005, J Appl Physiol, 98, 482-8.
324. Yang, Y., Creer, A., Jemiolo, B. and Trappe, S. 2005, J Appl Physiol, 98, 1745-52.
325. Raue, U., Slivka, D., Jemiolo, B., Hollon, C. and Trappe, S. 2006, J Appl
Physiol, 101, 53-9.
326. Stitt, T.N., Drujan, D., Clarke, B.A., Panaro, F., Timofeyva, Y., Kline, W.O.,
Gonzalez, M., Yancopoulos, G.D. and Glass, D.J. 2004, Mol Cell, 14, 395-403.
327. Pallafacchina, G., Calabria, E., Serrano, A.L., Kalhovde, J.M. and Schiaffino, S.
2002, Proc Natl Acad Sci U S A, 99, 9213-8.
328. McGee, S.L., Mustard, K.J., Hardie, D.G. and Baar, K. 2008, J Physiol,