Control of muscle bioenergetic gene expression: implications for allometric scaling relationships of glycolytic and oxidative enzymes
Department of Biology, Queen's University, Kingston, Canada K7L 3N6
* Author for correspondence (e-mail: moyesc{at}biology.queensu.ca)
Accepted 18 January 2005
Summary
Muscle metabolic properties vary with body size, with larger animals relying relatively less on oxidative metabolism as a result of lower specific activities of mitochondrial enzymes and greater specific activities of glycolytic enzymes. While many have argued reasons why such relationships might be grounded in physical relationships, an explanation for the regulatory basis of the differences in enzyme levels remains unexplored. Focusing on skeletal muscle, we review potential cellular and genetic explanations for the relationship between bioenergetic enzymes and body mass. Differences in myonuclear domain (the ratio of fiber volume to nuclei number) in conjunction with constitutive expression may explain part of the variation in mitochondrial content among fiber types and species. Superimposed on such constitutive determinants are (1) extrinsic signalling pathways that control the muscle contractile and metabolic phenotype and (2) intrinsic signalling pathways that translate changes in cellular milieu (ions, metabolites, oxygen, redox) arising through the contractile phenotype into changes in enzyme synthesis. These signalling pathways work through transcriptional regulation, as well as post-transcriptional, translational and post-translational regulation, acting via synthesis and degradation.
Key words: allometry, scaling, metabolic rate, mitochondria, glycolysis, citrate synthase, cytochrome c oxidase, nuclear respiratory factor, peroxisome proliferator activated receptor, PGC-1
Introduction
Allometric scaling and metabolic enzymes
For more than 100 years, physiologists have sought to explain the
allometric relationship between body mass and metabolic rate. The allometric
relationship is usually expressed as the equation
y=aMb, where the M is body
mass and the exponent b is the scaling coefficient, estimated
empirically as the slope of a log-log plot. While it is agreed that, in
general, larger animals have lower mass-specific metabolic rates than small
animals, the specifics of the relationship are vigorously debated. Many
questions revolve around defining robust, statistically valid mathematical
descriptions of data. Other questions relate to mechanistic explanations that
underlie the relationships. In this paper, we discuss the relationship between
the allometric patterns in the metabolic enzymes that support metabolic
rate.
The aerobic metabolic rate is met by flux through the mitochondrial pathway of oxidative phosphorylation. Thus, one might predict that an allometric pattern for mitochondrial enzymes in animals would reflect the pattern of aerobic metabolism. Unlike metabolic rate, which is a whole animal phenomenon, enzyme measurements are meaningful only when assessed in individual tissues. Most studies have focused on skeletal muscle enzymes because this is the tissue that likely contributes the greatest to basal metabolic rate. While the resting metabolic rate of skeletal muscle may be lower than many other tissues, it generally contributes the greatest to whole animal mass.
Surprisingly, there have been relatively few studies that have assessed the
allometric patterns in muscle enzymes. The first such study in tetrapods
(Emmett and Hochachka, 1981)
showed that muscle mitochondrial enzymes exhibited negative scaling; larger
animals had lower mitochondrial enzyme activities (see
Fig. 1A). Surprisingly, the
authors found that the scaling coefficient exponent for aerobic enzymes
(b=-0.11) was not as nearly as great as the exponent for aerobic
metabolic rate seen in most allometric studies (b ranges from -0.33
to -0.25). These muscle enzyme analyses also revealed a second paradox. Larger
animals were less oxidative, but they were also more glycolytic; glycolytic
enzymes showed positive scaling, with a scaling exponent of approximately +0.2
(see Fig. 1B). In hindsight,
this early study had some obvious weaknesses, such as the significance of
muscle fiber types, animal activity patterns and phylogenetic relationships
among the selected species. However, the fundamental conclusions have not been
refuted; in mammals oxidative enzymes show negative scaling and glycolytic
enzymes show positive scaling.
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Several subsequent studies examined muscle allometric patterns in fish. The
use of these animals largely avoids the confounding issue of muscle fiber type
heterogeneity. Fish models also provide an opportunity to examine
intraspecific variation in animals of similar geometry. In contrast to the
situation in mammals, mitochondrial enzymes of most fish are independent of
body mass (Somero and Childress,
1990; Burness et al.,
1999
; Norton et al.,
2000
). As in mammals, glycolytic enzymes show positive scaling,
with exponents ranging from 0.15 (barred sand bass;
Yang and Somero, 1996
) to 0.4
(rainbow trout; Somero and Childress,
1990
; Burness et al.,
1999
). Not all fish display the same patterns. Sea bass show
complex scaling patterns over different size ranges because of the way body
size influences locomotor style, hydrodynamic forces and muscle function
(Norton et al., 2000
). The
impact of locomotor styles is also seen in interspecies comparisons. Unlike
the active fish discussed previously, sedentary flatfish show negative scaling
for both mitochondrial (b=-0.68) and glycolytic (b=-0.44)
enzymes (Somero and Childress,
1990
).
Overall, these data suggest there is no single, simple relationship between
muscle enzymes and body size. Nonetheless, body size does appear to influence
muscle enzyme profiles in terms of both specific activity and relative
activities of glycolytic and mitochondrial enzymes. The purpose of this paper
is to review what is known about the regulation of bioenergetic enzymes in
muscle, focusing on explanations that may be directly relevant to allometric
patterns. However, given the dearth of information on the origins of
allometric patterns, we also draw on information from both genetic
interventions and development models. The differences in bioenergetics among
fiber types is of the same magnitude as the variation evident in allometric
studies. Mitochondrial enzymes are high in slow-twitch muscles and low in fast
twitch muscles; glycolytic enzymes are high in fast twitch muscles and low in
slow-twitch muscles. For example, rats show a sixfold range in citrate
synthase activity among skeletal fiber types, although most muscles fall
within a twofold range (Delp and Duan,
1996). For an allometric relationship with a scaling exponent of
-0.3, that same sixfold range would arise in animals differing 100-fold in
body mass.
Why do bioenergetic enzymes vary with body size?
Mitochondrial enzyme regulation has been the subject of many recent reviews
(summarized in Moyes and Hood,
2003). Based on these studies, we propose that the bioenergetic
phenotype is determined directly and indirectly by the regulators that
determine the contractile phenotype. We explicitly ignore the possibility that
broad genomic differences are needed to account for allometric variation.
Every animal has a genome that can produce tissues of differing metabolic
phenotype and can modify the profile in response to regulatory changes. We
argue that allometric patterns are a reflection of the plasticity inherent in
all muscles. To put this assumption another way, we expect that if the nucleus
of a shrew muscle were put into an elephant muscle, that shrew nucleus would
express its bioenergetic genes in a manner that was appropriate to build an
elephant's muscle. The environment of the elephant muscle would generate the
regulatory information to instruct the nucleus on the levels of gene
expression that were needed. Thus, the focus of this review is how contractile
activity is linked to pathways that control bioenergetic enzymes.
Cellular roles of glycolysis and mitochondrial metabolism
If a cell controls enzyme levels in a way that matches enzyme synthesis to intracellular needs, exactly when does it sample the available information to determine if the levels of glycolytic and mitochondrial enzymes are appropriate? The most common assumption is that the levels of glycolytic and mitochondrial enzymes are maintained at levels that are needed to meet the energy demands of active muscles. Recall, however, that the pathways have roles other than energy production and also function under different activity states.
The glycolytic pathway serves purposes beyond energy production during high intensity locomotion. Glucose transport and hexokinase are needed to produce muscle glycogen. Glycolytic intermediates are needed in the pentose phosphate pathway for production of reducing energy (NADPH) and nucleotides (ribose, deoxyribose). Glycolytic pyruvate can be imported and catabolized by mitochondria (glucose oxidation). The glycolytic pathway also produces energy under low flux conditions in the absence of oxygen. Despite these other roles, it makes sense that the levels of the muscle glycolytic enzymes are set at a level that is adequate to meet the infrequent demands of high intensity activity.
Mitochondria produce the energy for steady state muscle activity, but also
play important roles in resting muscle: production of energy, metabolic
intermediates, heat and reactive oxygen species. Since animals spend most of
their time at rest, it is possible that mitochondria are maintained at levels
primarily to meet the resting demands (see
Moyes et al., 1992). For
example, mitochondrial heat production is central to endotherm thermogenesis
and it is intriguing that mitochondrial enzymes scale in mammals (homeotherms)
but not fish (ectotherms). Since mitochondria produce more reactive oxygen
species at the lowest metabolic rates
(Korshunov et al., 1997
), the
lower mitochondrial content in muscles of large animals probably minimizes
superoxide production; if an elephant's muscle had the same mitochondrial
content as a mouse's muscle, its mitochondria would need to respire at rates
closer to state 4, which would exacerbate reactive oxygen species production.
For this reason, there may be linkage between the metabolic theory of aging
and allometric patterns. Life span of animals can be increased by treatments
that reduce oxidative stress, such as caloric restriction or genetic
manipulations that increase antioxidant enzymes activities (see
Beckman and Ames, 1998
). Thus,
it could be argued that the lower levels of mitochondria in muscles of large
animals is part of a successful strategy for a long life span, rather than
body size. With these caveats in mind, we think it is most likely that the
mitochondrial content of a muscle is controlled to enable the pathways to meet
the demands of steady state exercise.
If muscles control the bioenergetic phenotype to meet the demands of active metabolism, how does a muscle sense its own bioenergetic demand? In one scenario, which we term `extrinsic control', the external regulatory factors (e.g. hormones, neurotransmitters) that control the fiber-type contractile phenotype also exert secondary effects on metabolic enzyme synthesis by crosstalk. Thus, if thyroid hormones or insulin-like growth factors alter contractile protein gene expression, these same extrinsic factors may trigger appropriate changes in bioenergetic gene expression. In an alternative scenario (`intrinsic control'), the contractile phenotype (e.g. activity pattern, resting Ca2+ levels) itself induces changes within the muscle that alter the pattern of gene expression. The contractile state may exert its effects at rest or during activity. A fiber with slow muscle excitation-contraction coupling machinery creates a resting state that induces a change in bioenergetic gene expression that is appropriate for a slow muscle. Alternately, the contractile activity of muscles at their highest intensity may create perturbations that result in changes in bioenergetic gene expression.
Consider how one potential signal - Ca2+ concentration - differs in slow and fast muscles. Resting Ca2+ concentration is higher in a slow muscle than fast muscle because of the Ca2+ transport machinery, yet when contraction occurs, the peak Ca2+ levels are greater in fast muscle. These complex patterns in the Ca2+ signature at rest and during activity could encode regulatory information for pathways that control bioenergetic gene expression. Thus, it could be hypothesized that the enzymatic patterns arise as a response to the contractile properties of the muscle - the way it is constructed and the way it is used.
Genetic determinants of glycolytic and mitochondrial enzyme levels
Whatever ultimately determines the enzyme requirements for a tissue, the enzyme levels are maintained by a combination of protein synthesis and degradation. Enzymes, like other proteins, are synthesized by the sequential processes of transcription, mRNA processing and export, translation (initiation, elongation, termination) and post-translational modification (folding and assembly). Many mitochondrial enzymes have the additional complication of coordinating genomic regulation in two compartments: the nucleocytoplasmic and mitochondrial. Most recent studies assessing control of metabolic gene expression focus on factors that act via effects on transcriptional regulation. The challenge in this field of molecular genetics is unravelling networks that involve extrinsic regulators (endocrine, paracrine, autocrine, mechanical signals), signal transduction pathways (receptors, protein kinases, protein phosphatases), transcriptional regulators (DNA-binding proteins, coactivators, histone modifying enzymes) and target genes (enhancer elements, repressor elements).
In this era of high throughput mRNA measurements, there is a temptation to
focus on the role of transcriptional regulation in determining the patterns
seen in enzyme levels. This predilection leads to a focus on the role of
specific transcription factors. In another review on mitochondrial biogenesis
(Moyes and Hood, 2003), we
emphasize the potential role of other processes in determining enzyme levels:
post-transcriptional, translational and post-translational events, including
protein and RNA degradation. Consider the following example of the origins of
the positive allometeric scaling pattern in pyruvate kinase (PK) activity of
trout white muscle (Burness et al.,
1999
). In small fish (20-500 g), there is an excellent correlation
between PK mRNA and PK activity (Fig.
2), which is what would be expected when enzyme levels are
determined by transcriptional regulation. By contrast, the largest animals
(800-3000 g) had much more PK than would be predicted from their PK mRNA
levels. The two most obvious explanations for this observation were that (1)
the half-life of the protein was greater in the larger animals, such that less
mRNA was needed to maintain PK activity or (2) translational efficiency was
greater in larger fish, such that more protein was produced per unit
transcript. Thus, in this specific case, the muscle could achieve the expected
size-dependent increase in PK with a reduction in transcription of the PK
gene. The discrepancy between mRNA levels and protein levels is in fact quite
common. For example, barred sand bass display pronounced positive scaling with
LDH activity yet possess LDH mRNA levels that are independent of body mass
(Yang and Somero, 1996
).
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Constitutive versus inducible regulation
Glycolytic and mitochondrial enzymes are frequently considered housekeeping
functions, because virtually all tissues require both pathways. Yet the
specific activities of both pathways differ among tissues. Muscle enzyme
profile is under control of suites of transcription factors that vary among
muscles to establish and modify the metabolic phenotype. Despite the
tissue-to-tissue variation, cells are able to retain enzyme stoichiometries
within each pathway. These factors also serve to coordinate the expression of
the numerous genes that encode the enzymes and transporters needed to support
glycolysis and mitochondrial metabolism.
While the maximal capacities for oxidative phosphorylation may vary among tissues, the activities of most mitochondrial enzymes co-vary within narrow ranges. Similarly, the relative activities of most glycolytic enzymes are preserved, even when the maximal capacity of the pathway differs. It is thought that this regulatory feat (maintenance of stoichiometries) is ingrained by constitutive expression. Constitutive gene expression ensures that the cell produces a predictable baseline level of vital proteins needed for cellular function. Constitutive transcriptional regulation may allow gene expression to vary somewhat but normally only within narrow ranges. Superimposed on the constitutive pathway is inducible gene expression. Complex suites of transcriptional enhancers and repressors interact to adjust gene expression across a wide range, from suppressing constitutive expression to greatly inducing expression.
Thus, the levels of glycolytic and mitochondrial enzymes are each determined by a combination of constitutive and inducible regulation. In the context of allometric scaling, or any other comparison among muscles, it is difficult to parse out which aspect of this genetic variation accounts for the phenotype. One important parameter that may alter the way constitutive regulators determine the metabolic phenotype may be `myonuclear domain'.
Myonuclear domain co-varies with mitochondrial content
Myonuclear domain is the ratio of cytoplasmic volume per nucleus. In
comparison to slow twitch fibers, fast twitch fibers are larger, with a
greater myonuclear domain (Tseng et al.,
1994) and lower mitochondrial content. Thus, larger fibers have
fewer mitochondria and fewer nuclei per volume of tissue. Although myonuclear
domain is an ultrastructural parameter, it has ramifications for constitutive
expression.
Each myonucleus of a multinucleated myofiber is thought to govern the
nature of the cytoplasm within close proximity. There are no physical barriers
that confine mRNA regionally and any volume of cytoplasm is under the control
of both the nearest nucleus and a consortium of other nuclei in the region.
There is abundant evidence for the functional relevance of myonuclear domain
(see Allen et al., 1999). It
stands, therefore, that in an oxidative muscle (with more nuclei per volume of
cell than a glycolytic muscle) any region of cytoplasm has more nuclei within
close proximity. Thus, even if their myonuclei produced mRNA at the same rate,
an oxidative muscle could produce more bioenergetic enzymes per gram than
would a glycolytic muscle. For example, mitochondrial enzyme activities in red
versus white muscles of fish differ five- to tenfold when expressed
per gram tissue (an index of cytoplasmic volume) but only twofold when
expressed per milligram DNA (an index of nuclear content)
(Dalziel et al., 2004
).
Differences in myonuclear domain could allow muscles to achieve differences in
mitochondrial content without an alteration in constitutive gene
expression.
It is not yet known how myonuclear domain varies in homologous muscles of animals that differ in body size. Since the fiber diameter, enzyme profile and contraction kinetics are intertwined, we would hypothesize that myonuclear domain might explain at least part of the allometric pattern seen with mitochondrial enzymes. The influence of myonuclear domain is intriguing because it is a way to explain some of the differences in muscle mitochondrial enzymes without invoking tissue specific gene regulators. Of course, if differences in myonuclear domain help account for mitochondrial differences, they would also increase the need for regulatory increases in glycolytic gene expression.
Coordination of glycolytic gene expression
Control of glycolytic gene expression has been studied primarily in the
context of oxygen-sensing pathways (reviewed by
Semenza, 1998). When cells
become hypoxic, the protein HIF-1
(hypoxia inducible factor-1
)
is stabilized and rapidly accumulates. At the molecular level, hypoxia
prevents the hydroxylation of a critical proline residue, which normally
entices the ubiquination machinery to tag the protein for degradation. In the
absence of accelerated degradation, the protein accumulates and associates
with its binding partner HIF-1ß. The HIF heterodimer is a DNA-binding
protein that associates with regulatory hypoxia-sensitive elements to
accelerate transcription. Recognition sites for HIF-1 can be found in the
promoters of most glycolytic genes, as well as other genes that must respond
to hypoxic conditions, such as erythropoietin. Thus, the HIF-dependent
stimulation of gene expression is an elegant pathway by which a coordinated
`increase' in gene expression can be achieved. However, there is little
evidence that the same pathway controls glycolytic gene expression under
normoxic conditions and by extension there is no reason to believe that HIF
plays a role in establishing the allometric pattern.
The relationship between HIF activities, HIF-1 levels and body size
remains to be addressed experimentally. But if HIF does not determine
glycolytic enzyme levels under normal conditions, then what does?
Table 1 summarizes the
transcription factors that are thought to regulate each of the glycolytic
genes. As is the case with many genes, glycolytic genes share sensitivity to
the transcription factors of the Sp1 (specificity protein 1) family. The most
important members - Sp1 and Sp3 - bind to the same GC-rich element in the
proximal promoter. When Sp1 or Sp3 attach to DNA, they also bind proteins of
the general transcriptional machinery, which must assemble to initiate
transcription. Sp1 binding usually stimulates transcription whereas Sp3
binding can stimulate or inhibit transcription. Regulation by Sp1 and Sp3 is
complex because of their interactive effects and multiple modes of
post-translation modification (reviewed by
Li et al., 2004
). For example,
Sp3 binds the promoter as a monomer, whereas Sp1 can form multimers that allow
Sp1 sites to act synergistically. If Sp3 binds to the promoter, it prevents
Sp1 multimers from binding multiple sites, disrupting their synergy. Truncated
forms of Sp3 can also bind the same element; they do not enhance
transcription, thereby acting as repressors. Members of the Sp1 family can be
modified by phosphorylation, glycosylation and acetylation - modifications
that may promote or impair their transcriptional effects.
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The complexity of Sp1 signalling makes it difficult to assess if it is
responsible for the patterns of glycolytic enzymes among tissues and species,
including allometric variation. Within a given muscle nucleus, a particular
Sp1 family profile may have different effects on each glycolytic gene. First,
the affinity of Sp1 and Sp3 for their element depends on the context (the
sequence of DNA that flanks the element). Thus, a particular nuclear ratio of
Sp1/Sp3 could enhance a gene with a single Sp1 site, where both Sp1 and Sp3
act in a similar fashion, but inhibit the expression of a gene with multiple
tandem Sp1 sites, where Sp3 disrupts the synergistic action of Sp1 multimers.
As a given Sp1 profile can have different effects on genes within the same
nucleus of a muscle, it is difficult to predict the consequences when the Sp1
profile changes in phenotypic adaptation or differs among fiber types. When
genomic/interspecies differences are included in the mix, a formidable task
approaches the impossible. Two species could, in principle, differ in the
promoter context of each glycolytic gene, as well as the transactivational
properties of the different Sp1 family members. While the structure of
transcription factors tends to be conserved across broad taxonomic comparisons
(Hsia and McGinnis, 2003),
there are important examples of evolutionary variation in the Sp1 gene family
(Kolell and Crawford, 2002
).
Thus, there are many experimental obstacles to assessing genetic explanations
for the allometric variation in glycolytic genes across phylogenetically
diverse taxa. Furthermore, it remains unknown how a muscle might transduce a
metabolic signal into a change in Sp1 transactivational activity.
Coordination of mitochondrial gene expression
Mitochondrial biogenesis requires coordinated transcription of nuclear and
mitochondrial genes (Garesse and Vallejo,
2001; Kelly and Scarpulla,
2004
; Scarpulla,
2002
). Coordination of gene expression is achieved through several
transcriptional regulators that regulate suites of genes: nuclear respiratory
factor 1 (NRF1), NRF2, the peroxisome proliferator activated-receptors (PPARs)
and the PPAR
coactivator-1
(PGC-1
). The reader is
directed to the many excellent reviews that discuss these transcriptional
regulators in more detail.
NRF1 is a homodimeric DNA-binding protein that was first identified as an
activator of the rat somatic cytochrome c gene and has since been
shown to regulate multiple genes involved in mitochondrial oxidative
phosphorylation and proliferation (see
Kelly and Scarpulla,
2004).
NRF2, a member of the ETS family of transcription factors, is a
heterotetramer composed of two DNA-binding subunits () and either two
ß subunits or two
subunits (see Kelly and Scarplla, 2004). NRF2
was identified as an activator of rat COXIV but has since been shown to
regulate multiple genes associated with mitochondrial biogenesis and oxidative
metabolism.
The PPARs are a subfamily of the nuclear hormone receptor superfamily
composed of three members: , ß/
and
(for reviews,
see Berger and Moller, 2002
;
Gilde and Van Bilsen, 2003
).
The PPARs heterodimerize with a retinoid acid receptor. In the presence of
agonists, the heterodimer binds PPAR response element in the promoter sequence
of target genes to activate transcription. PPAR
and ß/
are
the most prevalent isoforms in striated muscle, where they regulate genes
related to lipid metabolism.
The PGC-1 family of transcriptional coactivators includes
PGC-1
, PGC-1ß and PGC-1-related coactivator (PRC) (for reviews,
see Knutti and Kralli, 2001
;
Puigserver and Spiegelman,
2003
). PGC-1
is recruited by transcription factors that are
bound to the promoter of target genes. The complex then recruits other
effectors, increasing the transcriptional activity. PGC-1
was first
identified as a cold-inducible coactivator of PPAR
in adipose tissue
(Puigserver et al., 1998
). It
has since been shown to interact with several of the transcriptional
regulators that control mitochondrial gene expression and is now considered to
be a master coordinator of mitochondrial biogenesis. PGC1
has also been
shown to stimulate the expression of genes encoding slow muscle contractile
proteins, providing a potential link between the contractile and bioenergetic
phenotypes (Lin et al., 2002
).
Recently, a PGC1
mouse was created and displayed 30-60% shortfalls in
muscle mitochondrial gene expression, energetic defects and a deficiency in
shivering thermogenesis (Lin et al.,
2004
). While this attests to the importance of PGC1
, even
the null mutants were viable and possessed mitochondria, perhaps reflecting
the role of other PGC1 family members.
These transcriptional regulators serve to coordinate the many hundreds of
genes required for mitochondrial biogenesis. There is natural variation in the
levels of these transcriptional regulators among muscle fiber types that
correlates with mitochondrial content (see
Moyes and Hood, 2003). In most
cases, transgenic studies that over-expressed one of these transcriptional
regulators led to an increase in the levels of selected mitochondrial
precursors (see Table 2).
However, the importance of these transcriptional regulators in determining
allometric relationships has not been studied. Given the complexity of the
mechanisms (multiple members of gene families, complex heterodimerization
patterns, ligand-dependent interactions), it would be challenging to use a
comprehensive transcription factor profile to predict a resulting bioenergetic
profile.
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Linking bioenergetic enzyme gene expression with the contractile phenotype
Earlier in this review we distinguished between intrinsic and extrinsic determinants of bioenergetic gene expression. Intrinsic pathways begin when a muscle senses an intracellular condition that triggers a change in the synthesis or degradation of enzymes. Extrinsic regulators exert effects on muscle development to establish an appropriate metabolic phenotype to support the contractile phenotype. Most studies that investigate the roles of these potential regulators focus on a single pathway. In the context of allometry, we are on the lookout for a factor that could trigger reciprocal changes in glycolytic and mitochondrial gene expression.
Intrinsic control of bioenergetic gene expression
How does a muscle cell sense a need to alter gene expression? Most recent
research has focused on the roles of four surrogates of muscle activity: redox
status, hypoxia, Ca2+ and AMP. While there is some merit in
hypotheses that implicate these regulators in determining metabolic gene
expression, there is no evidence that a single regulator causes reciprocal
changes in glycolytic versus oxidative enzymes, as seen in allometric
relationships.
Redox regulation is a term used in multiple contexts (reviewed by
Leary and Moyes, 2000). In
some cases, oxidative stress can trigger changes in gene expression through
affects on the transcription factor NF-E2-related factor-2. The acronym for
NF-E2-related factor-2 is Nrf2, which is unfortunately quite similar to that
of an unrelated transcription factor, nuclear respiratory factor 2, or NRF 2.
It binds to antioxidant response elements in the promoters of many genes
related to antioxidant protection. This type of redox regulation is intended
to compensate for cytotoxic effects of xenobiotics, but another type of
oxidative stress is thought to be important in the regulation of bioenergetic
genes. When mitochondria are defective or inhibited, there can be an increase
in the production of superoxide at complex I and III. This type of oxidative
stress may contribute to neurodegenerative diseases, but it may also signal
the nucleus that there are mitochondrial shortfalls (see
Leary and Moyes, 2000
). A
third connotation of redox regulation refers to the metabolic redox state,
whereby changes in NADH/NAD+ may be communicated to regulatory
enzymes. Recently, an NAD+-dependent histone modifying enzyme,
Sir2, was shown to be sensitive to NADH/NAD+ ratios, which may
contribute to control of gene expression in response to metabolic conditions
(Fulco et al., 2003
).
Hypoxia stimulates glycolytic gene expression through increases in HIF-1,
as discussed earlier in this review. HIF-dependent gene regulation probably
plays a role in stimulating muscle remodelling in intense exercise, more so at
high altitude (Vogt et al.,
2001). While HIF may induce adaptive remodelling, it is unlikely
to be responsible for establishing basal levels of glycolytic enzymes under
normoxic conditions. There is no evidence that mitochondrial genes are
responsive to HIF, although it alters the expression of proteins that
facilitate oxygen delivery and could affect mitochondrial metabolism
indirectly.
Ca2+ plays an important role in control of contraction, but it
may also help tailor muscle bioenergetic gene expression. The nature of the
Ca2+ signal is complex because of the temporal, spatial and
quantitative variation in Ca2+, known as the Ca2+
signature. Changes in the Ca2+ signature influence a multitude of
signalling pathways in cardiac muscle (see
Bers, 2002) and skeletal muscle
(see Michel et al., 2004
).
Increases in Ca2+ in L6 cultured myoblasts triggers an increase in
expression of several transcription factors implicated in mitochondrial gene
expression, including NRF-1, NRF-2 and PGC-1
(Ojuka et al., 2003
). When at
rest, oxidative muscles tend to have higher levels of Ca2+ (100-300
nmol l-1) than do glycolytic muscles (50 nmol l-1)
(Chin and Allen, 1996
). This
difference in resting Ca2+ levels may induce the changes in
expression of bioenergetic genes, acting through CaN
(Michel et al., 2004
) and CaMK
(Wu et al., 2002
). While both
CaN and CaMK can each trigger a transformation of fast skeletal muscle fibers
to slow skeletal muscle fibers (e.g.
Talmadge et al., 2004
;
Wu et al., 2002
), they can
exert subtly different effects on bioenergetic enzymes, at least in heart
(Schaeffer et al., 2004
). CaN
activates expression of fatty acid oxidizing genes and selected mitochondrial
proteins, without affecting glycolytic genes. by contrast, CaMK transfection
has little effect on fatty acid oxidizing genes but increases the expression
of mitochondrial genes as well as the glucose transporter 4 and
phosphofructokinase. While both CaN and CaMK increase PGC-1
expression,
only CaN increases PPAR
expression
(Schaeffer et al., 2004
).
The final metabolic regulator we consider is the adenylate pool. Under
conditions of high ATP turnover, AMP levels increase due to the activity of
myokinase (2ADP ATP + AMP). When AMP:ATP ratios increase, AMP-activated
protein kinase (AMPK) is activated, phosphorylating many proteins that are
involved in intermediary metabolism (see
Hardie et al., 2003
),
including skeletal muscle mitochondrial enzymes
(Winder et al., 2000
). There
is also evidence that AMPK activity can be modulated by phosphorylation,
independent of AMP:ATP ratios (see Carling,
2004
). Several studies have shown that AMPK can affect gene
expression (see Hardie et al.,
2003
), but the exact signalling pathways have not yet been
identified.
Extrinsic factors determine bioenergetic profile in muscle differentiation and development
In contrast to adaptive remodelling, the metabolic profile that arises
during muscle differentiation and development is likely under the control of
extrinsic factors. Undifferentiated muscle cells (myoblasts) derive
approximately 80% of their energy from glycolysis. Serum starvation triggers
the myogenic program, initiated by autocrine production of insulin-like growth
factor II. Within the first couple of days of differentiation, the myocytes
shift their metabolic poise and derive almost 80% of their energy from
mitochondrial metabolism, however the metabolic rate is not altered. Two weeks
post-differentiation, myotubes have increased mitochondrial content about
fivefold, but glycolytic enzymes do not change
(Moyes et al., 1997). The
reason for this metabolic shift in the first few days of differentiation
remains enigmatic because it occurs well before major changes in mitochondrial
enzymes (Lyons et al., 2004
).
In any case, the changes in mitochondrial enzymes during myogenesis appear to
arise independent of metabolic changes, arguing against a role for the
intrinsic regulators in myogenic mitochondrial proliferation.
Once the muscle has matured, many hormonal regulators can trigger muscle
remodelling. Transgenic studies that employ active forms of CaN and CaMK
implicate these enzymes in muscle remodelling, but activation in normal muscle
would require a favourable change in Ca2+. As mentioned, fast and
slow muscles have different Ca2+ levels
(Chin and Allen, 1996) as a
result of the contractile phenotype (Ca2+ transporters,
Ca2+ sequestration, parvalbumin). Thus, the question ultimately is
what causes the change in the contractile phenotype. Muscle fiber-type
transformation can arise in response to innervation patterns,
mechanosignalling pathways, autocrine IGF-II production and thyroid hormone
state (see Baldwin and Haddad,
2002
). Understanding the patterns of allometric scaling of enzymes
then would need to focus on the hormones and other signals that determine the
contractile phenotype, which then affects the metabolic phenotype.
Perhaps more provocative are the interventions that lead to reciprocal
changes in mitochondrial and glycolytic enzymes, analogous to the differences
seen with body size. Hughes et al.
(1999) showed that
overexpression of the myogenic factor myogenin induced reciprocal changes in
mitochondrial and glycolytic enzymes of fast muscle, but without a change in
the contractile phenotype, as indicated by myosin heavy chain profile. Unlike
the other studies summarized in Table
2, myogenin is one of the few transgenes that can cause reciprocal
changes in the two pathways. Similarly, metabolic disorders including obesity
and type II diabetes trigger reciprocal changes in glycolytic enzymes
(Hickey et al., 1995
;
Tanner et al., 2002
).
Future directions
Many recent studies have identified signalling enzymes and transcriptional regulators that control mitochondrial gene expression. Regrettably, few of these studies measure the effects of the transgene on glycolytic enzymes. At the same time, enough is known about these regulators to perform comparative analyses in the context of allometric variation. Unfortunately, those species that differ most in body size are only distantly related, complicating analyses that may be sensitive to genomic variation. We see progress in this area dependent upon several related approaches. (1) More studies of muscle biochemistry in specific fiber types of animals of different sizes. There are many studies that have assessed allometric patterns in metabolic rate of tetrapods, very few studies have explored muscle biochemistry in this context. (2) More fish studies linking metabolic rate and muscle biochemistry. Muscle biochemistry in relation to body size is better studied (and perhaps best studied) in fish, but in these animals metabolic rate measurements are more problematic. (3) Molecular genetics of bioenergetic gene expression. As we learn more about the control of both mitochondrial and glycolytic genes, it will become possible to explore the regulatory basis of comparative patterns of muscle gene expression. While studies will likely focus on the role of transcriptional regulation, other factors such as myonuclear domain, translational and post-translational regulators must also be considered.
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