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First published online March 14, 2008
Journal of Experimental Biology 211, 1041-1049 (2008)
Published by The Company of Biologists 2008
doi: 10.1242/jeb.013722
Persistence of motor unit and muscle fiber types in the presence of inactivity
1 Brain Research Institute, University of California, Los Angeles, CA
90024-1761, USA
2 Department of Biological Sciences, Northern Arizona University, Flagstaff, AZ
86001-5640, USA
3 Department of Physiological Science, University of California, Los Angeles, CA
90024-1761, USA
4 Department of Physiology and Biophysics, University of California, Irvine, CA
92697, USA
* Author for correspondence (e-mail: rrr{at}ucla.edu)
Accepted 4 February 2008
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Summary |
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-glycerophosphate dehydrogenase activities were determined for a sample
of motor unit and non-motor unit fibers, providing a measure of three enzyme
activities often used to characterize fiber phenotype within a single unit.
Although normal enzyme activities were altered after 6 months of inactivity,
the relationships among the three enzymes were largely maintained. These data
demonstrate that it is not the diversity in any single enzyme property but the
profile of several metabolic pathways that underlies the significance of fiber
phenotypes. These profiles must reflect a high level of coordination of
expression of selected combinations of genes. Although neuromuscular activity
level influences fiber phenotype, the present results demonstrate that
activity-independent mechanisms remain important sources of the control of
phenotype establishment in the near absence of activity.
Key words: 3-D representations, motor unit types, succinate dehydrogenase, myofibrillar adenosine triphosphatase, -glycerophosphate dehydrogenase
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INTRODUCTION |
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;
Brooke and Engel, 1969;
Peter et al., 1972). Using
this approach one could characterize each fiber with respect to its relative
enzyme activity or substrate concentration profile. The principal result from
this approach was that in hindlimb skeletal muscles of several mammals there
generally appeared to be approximately three identifiable populations of
fibers based on myofibrillar adenosine triphosphatase (mATPase) and a series
of enzymes associated with glycolytic and oxidative metabolism and the
electron transport chain within the mitochondria
(Peter et al., 1972).
One major difficulty with these results was the lack of quantification.
Initially, the major contribution of this procedure was to give a clear
impression of the types of fiber that could be identified reasonably
consistently. Soon thereafter it became possible to quantify similar
properties at the whole muscle level by using classical biochemical
techniques, i.e. to quantify enzyme activity rates of homogenized muscle
tissue in the test tube, and to measure physiological properties of the same
muscle, thus providing a more direct indication of the basic mechanisms
controlling the contractile properties of the muscle such as speed and
fatigability (Barnard et al.,
1971). Subsequently, the same approach was applied in studies of
single motor units, whereby the contractile and biochemical properties of the
fibers innervated by a single motoneuron could be determined
(Bodine et al., 1987;
Burke et al., 1973;
Edstrom and Kugelberg, 1968).
The same basic concept and conclusion evolved from each of the approaches
noted above. Specifically, it became clear that among the hindlimb mammalian
skeletal muscles studied, the motor units and muscle fibers in normal control
muscles generally could be categorized into the following three populations:
(1) fast contracting units and fibers that were very fatigable; (2) fast
contracting units and fibers that were relatively fatigue resistant; and (3)
slow contracting units and fibers that were very fatigue resistant
(Burke et al., 1973;
Burke and Edgerton, 1975;
McDonagh et al., 1980;
Peter et al., 1972).
This generalized concept that mammalian skeletal muscles of the hindlimb
fall into three categories has limitations. In fact, a contrasting view is
that there is little validity to a generalized concept relating to muscle
fiber types (Pette and Staron,
1990) because there are many more combinations of enzyme activity
levels among single muscle fibers than can be accommodated by a tripartite or
even a tetrapartite (McDonagh et al.,
1980) categorization. A second reason for this position is that
the level of plasticity of single fibers is so dynamic that it is futile to
attempt to categorize them into identifiable clusters. Pette and Staron
(Pette and Staron, 1990)
suggested that there are at least as many fiber types as there are motor units
in a muscle.
The issues of whether there are consistently identifiable types of fiber
and, if there are, how many and to what extent does this categorization depend
on the activity of the motor units, are long standing
(McDonagh et al., 1980). In a
large part, however, the views expressed by different investigators have not
been based on clearly identified quantitative criteria for distinguishing
fiber types. The purposes of the present paper were to address three questions
related to these issues. Firstly, can statistically defined populations
(types) of fibers be identified using quantitative measurements of three
enzyme systems often associated with fiber phenotypes in the cat tibialis
anterior (TA) muscle? Secondly, how are these muscle fiber types related to
motor unit types, i.e. are muscle fiber phenotypes linked to the physiological
properties of motor units in a muscle? Thirdly, does a severe perturbation in
the activation of motor units, a function often proposed to be the dominant
controlling factor in the expression of muscle fiber phenotypes, result in a
level of plasticity such that neither muscle fiber phenotypes nor motor unit
types can be identified?
The present results indicate that three identifiable populations of fibers
exist in the cat TA based on the quantification of three enzyme systems. The
inter-relationships among a marker enzyme in each of three protein systems
that reflect a different metabolic property, i.e. mATPase (a marker of
contractile function), succinate dehydrogenase (SDH, a marker of oxidative
capacity) and -glycerophosphate dehydrogenase [GPD, an enzyme involved
in the NADH/NAD exchange between the mitochondria and the cytoplasm that is
highly correlated with the glycolytic capacity of a muscle
(Peter et al., 1972)], clearly
show three fiber-type populations when linked to the myosin isoform as shown
previously in cat hindlimb muscles
(Edgerton et al., 1985;
Roy et al., 1996). In
addition, although a 6 month period of near inactivity induced by spinal cord
isolation (SI) resulted in shifts in the enzyme activities within these three
systems, three populations of fibers persisted in the TA of SI cats.
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MATERIALS AND METHODS |
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TOPSummaryINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONReferences |
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).
Under deep pentobarbital anesthesia (35 mg kg–1) and aseptic
conditions, a partial laminectomy was performed from T12 to S2 and a bilateral
dorsal rhizotomy was performed intradurally between T12 and S2. The spinal
cord was completely transected at the junctions of T12–T13 and again at
L7–S1. These procedures result in near inactivity of the hindlimb
muscles for prolonged periods in cats
(Eldridge et al., 1981;
Pierotti et al., 1991) and
rats (Roy et al., 2007). The
procedures for the post-surgical care of spinal-injured cats are detailed in
Roy et al. (Roy et al.,
1992).
Approximately 6 months following surgery, one motor unit in the TA muscle
of four control and nine SI cats was successfully characterized
physiologically and then the fibers belonging to the motor unit were glycogen
depleted by stimulating its functionally isolated axon as described previously
(Pierotti et al., 1991). All
procedures were approved by the Animal Use Committee at UCLA and followed the
American Physiological Society Animal Care Guidelines.
Histological procedures
At the end of the physiological testing [results reported in Pierotti et
al. (Pierotti et al., 1991)],
the muscles were excised and prepared for histological analyses. Each muscle
was cut transversely into 
5 mm blocks over its entire length, and the
blocks were mounted on cork and rapidly frozen in isopentane cooled with
liquid nitrogen. To assess the glycogen content of the muscle fibers,
cross-sections (20 µm thick) were cut and stained for the periodic
acid–Schiff reaction (Pearse,
1961). Briefly, the sections were placed in solution (8.0 ml 100%
ethanol, 1.5 ml chloroform and 0.5 ml glacial acetic acid, pre-cooled in a
cryostat at –20°C for 30 min), left in the cryostat for 5 min, and
then moved to room temperature for 10 min. The sections were rinsed in
distilled H2O and transferred into a 0.5% periodic acid solution
for 5 min. The sections then were rinsed with distilled H2O and
incubated in Schiff's reagent (Sigma-Aldrich Corp., St Louis, MO, USA) at
37°C for 10 min. The sections were then air dried overnight and mounted
with aquamount. An image-processing computer system was used to determine the
optical density of glycogen staining for each of the outlined fibers
(Cope et al., 1986). The very
low optical density level of glycogen staining identified the muscle fibers
belonging to an isolated motor unit. Quantitative measures of SDH and GPD were
determined histochemically in a sample of motor unit fibers and a sample of
non-motor unit fibers (fibers not depleted of glycogen and thus exhibiting
relatively high levels of glycogen staining) located within the motor unit
territory (Martin et al.,
1988). In addition, a modification of the histochemical method of
Weisberg et al. (Weisberg et al.,
1982) was used to measure the mATPase activity
(Jiang et al., 1991) in the
same fibers.
Fiber enzyme activity levels were determined from digitized images of the
muscle cross-sections that were stored as gray-level pictures. An
image-processing system determined the light transmittance for each pixel,
which was subsequently converted to an optical density. The optical density
reading per fiber was calculated as an average of all pixels within the fiber
boundaries. The SDH, GPD and mATPase reactions were terminated while the
reactions were still linear. Thus the enzymatic activities were expressed as
steady-state rates, i.e. optical density min–1. The activity
of each analyzed fiber was expressed as the mean of three sections incubated
with substrate minus the mean of two sections incubated without substrate.
This procedure corrected for any non-specific staining that may have occurred
during the reaction. These procedures have been used routinely in our
laboratory (Edgerton et al.,
1990; Graham et al.,
1992; Jiang et al.,
1991; Martin et al.,
1988; Pierotti et al.,
1994; Rivero et al.,
1999).
Immunohistochemical procedures
Individual fibers were typed using monoclonal antibodies specific for
myosin heavy chain isoforms. Antibodies BF-F8, BF-13 and BF-35 (kindly
provided by Dr S. Schiaffino, University of Padova, Italy) were used in this
study and their specificity in rat
(Schiaffino et al., 1989) and
cat (Talmadge et al., 1996)
muscles has been described previously. Three fiber types are present in cat
hindlimb muscles: type I (positive for BF-F8 and BF-35 and negative for
BF-13), type IIa (negative for BF-F8 and positive for BF-35 and BF-13) and
type IIx (negative for BF-F8 and BF-35 and positive for BF-13). Frozen
sections (10 µm thick) were incubated for 30 min in a blocking solution
containing 1% bovine serum albumin in phosphate-buffered saline (PBS) and then
overnight at 4°C with the monoclonal antibodies. A Vectastain ABC kit
(Vector Labs, Burlingame, CA, USA) was utilized to amplify the
antigen–antibody complex. After a 20 min wash in PBS, secondary
antigen–antibody complexes were detected by incubation for 30 min with a
biotinylated antibody to mouse immunoglobulins. Each tissue section was washed
for 20 min with PBS. The antigen–antibody complex was visualized by
treatment with diaminobenzidine and hydrogen peroxide for 10 min. The sections
were dehydrated in 70, 95 and 100% ethanol, followed by xylene and mounted
with permount. In the present study almost all fibers could be classified as
type I, IIa or IIx fibers. Less than 1% of the sampled fibers were classified
as `hybrid' fibers (all non-motor unit fibers) and these were excluded from
the study. In addition, there was a rare occurrence of fibers showing signs of
degeneration (fragmentation, central nuclei, etc.) in some muscles of each
group, including the control group. None of these were motor unit fibers and
they were excluded from the study.
Statistical analyses
To make comparisons among muscle fibers across animals, the absolute enzyme
activities were normalized (z-scores) to the mean for each muscle.
Since three enzyme activities were measured in each fiber, a multivariate
analysis of variance (MANOVA) was used to test whether (1) fibers from the
experimental group differed from fibers in the control group, (2) fiber types
across experimental groups differed from one another, and (3) there was an
interaction between group and fiber type. The most appropriate procedure for
determining whether the data from the three distinct fiber types differed
significantly was MANOVA. Rather than performing three separate ANOVAs, one on
each of the variables (mATPase, SDH and GPD), MANOVA provided a single test of
whether the three fiber types differed in the multivariate sense (capitalizing
on the covariance structure of the data). Separate ANOVAs would have much less
statistical power. In effect, MANOVA can allow the null hypothesis to be
rejected under conditions in which the separate ANOVAs cannot. A Hotelling
procedure was used to compare data from control and SI rats within fiber type.
Statistical significance was set at P
0.05 for all
comparisons.
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Given three distinct sets of multivariate data, a graphical depiction was desired to show a multivariate confidence region for each set. This required the reconstruction of a boundary such that (1) data within the boundary probably belonged to that set, and (2) data outside of the boundary were far enough removed from the centroid that they were regarded as outliers. Rather than focusing on one variable at a time, a probability density measure was used to take into account the covariance structure of the data within each set. The sets of data were generated identifying three muscle fiber types based on three variables (mATPase, SDH and GPD). For each set of data (all muscle fibers of a given type), it was possible to establish confidence regions for values on each variable separately by calculating z-scores. In this case, the distance of an item from the mean of one variable was expressed in terms of the standard deviation units for that variable. Setting the criterion at two standard deviation units, it was possible to judge an item to be an outlier if it was removed more than two units from the mean of each of the three variables. This procedure was considered appropriate since it ignored the covariance between the three variables.
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RESULTS |
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TOPSummaryINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONReferences |
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10 to 50% of
the total range for all fibers for both control and SI muscles. Overall, the
enzyme activity of fibers within a motor unit occupied 25% of the total
range that was observed among all fibers in both control and SI muscles
(Pierotti et al., 1994).
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The relationship between mATPase and SDH activities within motor unit fibers for the normalized data from the control (Fig. 1A) and SI (Fig. 1B) cats is plotted by myosin heavy chain type. Note the same general pattern for the three fiber types for the control and SI data. While there is clear overlap between the three fiber-type groups when comparing two enzyme activities with a known myosin phenotype, there are also apparent clusters of fibers in both graphs. Type I and IIx fibers comprise the most distinct groups with respect to the mATPase/SDH ratio. The relationship between mATPase and GPD activities for motor unit fibers in control and SI muscles is shown in Fig. 2A and B, respectively. Again the type I and IIx fibers comprise the most distinct groups with respect to the mATPase/GPD ratio in both groups, with overlap in either group largely due to type IIa fibers.
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The omnibus test of whether the fiber types differed in the multivariate sense was based on the Wilks' lambda test. This gave F(6.1538)=315.0342 (P<0.0001). The first discriminant function accounted for 83% of the variance due to differences among fiber types. For the sake of completeness, a MANOVA was performed to test whether there was a difference between the control and SI animals, and there was no significant difference [F(3.769)=0.43, P=0.73]. Interestingly, the interaction between group (control and SI) and type was significant [F(6.1538)=8.0455, P<0.0001]. Therefore, the SI manipulation influenced the pattern of activities, i.e. the relationship among the activities of mATPase, SDH and GPD between different fibers, although it had no main effect on those activities.
The nature of this interaction was examined by looking at the distances
between the centroids. All six centroids differed on the three activity
measures, but these differences were reduced to a simple Euclidean distance
measure for each pairwise comparison of centroids on those three dimensions.
These distances then were submitted to classical metric scaling
(Torgerson, 1958) to derive
two new dimensions upon which to plot the six points corresponding to the
factorial crossing of two groups with three types. The six points were plotted
to illustrate how a large interaction could occur when there was no group
effect (Fig. 7). Inactivity had
little effect on type I fibers, reduced type IIa fibers on dimension 2, and
increased type IIx fibers on both dimensions 1 and 2. The contrary motion
between the type IIa and type IIx fibers nullifies the group main effect.
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DISCUSSION |
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TOPSummaryINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONReferences |
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), the overlapping regions between groups has led to one
viewpoint: that there is little significance to the concept of muscle fiber
types. The expression of multiple protein isoforms within a single fiber
(Caiozzo et al., 2003;
Roy et al., 1997;
Staron and Pette, 1986;
Staron and Pette, 1987;
Talmadge et al., 1999;
Stephenson, 2001) clearly
shows a greater diversity than once was apparent among mammalian fibers. This
diversity raises the issue of the biological significance of the concept of
muscle fiber types (Pette and Staron,
1990). As noted earlier, the question is: are there as many fiber
types as there are motor units in a muscle?
The present data set is unique in that we examined the combination of three
protein systems within single muscle fibers of physiologically defined motor
unit types. These three protein systems represent those around which gene
expression in mammalian muscles is proposed to be organized
(Burke and Edgerton, 1975;
Edgerton and Simpson, 1969).
We also asked the question whether the dynamic plasticity of muscle fibers
makes classification of muscle fibers and motor units into types essentially
irrelevant. If the neuron plays an important role in modulating the proteins
within a single muscle fiber as well as within a single motor unit, then
modulation of neuromuscular activity should also affect the identification of
specific fiber types at the level of organization of the motor unit. The
issues specifically addressed in the present study were whether a muscle fiber
could be categorized as statistically unique based on the measurements of the
three metabolic enzymes studied, whether these metabolic markers are unique to
a motor unit type and, furthermore, whether these levels of organized states
would still be applicable following 6 months of electrical silence.
Our results indicate that the activity levels of markers of three enzyme systems can be used to identify different populations of fibers if the myosin phenotype is known. Although a detailed characterization of other parameters may further enhance the differences among the clusters (fiber types), the protein systems studied in the present paper may represent those with which many other protein systems are co-regulated. The multivariate analysis allowed us to examine the covariance characteristics among fibers as a function of motor unit identification. The relationship of one variable to another defines a very strict limit on the group in which a fiber may be placed, in much the same way that the relationships among variables dictate the functional limits of the fibers in question.
Based on the MANOVA analyses, we conclude that there were three groups of fibers for both control and SI populations. Furthermore, if the population of muscle fibers in the present data set is sub-divided based on myosin type and motor unit type, we conclude that statistically distinct types still can be identified. Although near-complete inactivity was imposed on the SI muscles and adaptations occurred with respect to the individual enzyme systems, the relationships among the systems were altered without blurring a striking distinction among the fiber and motor unit types. The fact that these distinctions are present at the whole muscle and motor unit levels strengthens the argument for distinct types of muscle fiber and motor unit and that this distinction persists in almost the complete absence of neuromuscular activity.
While the presence of a continuum among fibers of a given muscle for any
single variable is apparent, a single variable cannot describe the functional
diversity of individual muscle fibers. We propose that the biological
significance of motor unit and muscle fiber types is in the coordination of
the expression of genes that define the protein levels for the interactive
protein systems. It is apparent that the proteins within any given system will
be coordinated, e.g. enzymes that carry out glycolysis, glycogenolysis, etc.
But the significance of the concept of muscle fiber types must be that there
is a substantial coordination of the expression of genes within and among
protein systems. There is a high level of interdependence of the expression of
type I and type II myosin. If type I myosin expression is depressed in a
fiber, a type II isoform will be enhanced and vice versa
(Baldwin and Haddad, 2001).
Although multiple isoforms are expressed in some fibers, even in muscles of
control animals (Caiozzo et al.,
2003; Roy et al.,
1997; Staron and Pette,
1986; Staron and Pette,
1987; Talmadge et al.,
1999), there is a high probability that the expression of one will
dominate in a fiber adapted to a steady state. In the present study, there was
a rare occurrence of hybrid fibers in the TA of control or SI cats.
It is significant that the net effect beyond this extensive coordination of
genes within a fiber and among fibers of a motor unit results in the
considerable diversity in function among motor units. Without this gene
coordination there would be diversity in the biochemical properties, but no
coordination of gene expression or function at either the fiber (and therefore
no fiber types) or motor unit level. It is this coordinated diversity among
fibers within a muscle that defines the potential of the motor units (and
fibers) of a given muscle to produce work at a given power, duration and
pattern. If a fiber expresses a slow myosin isoform, its GPD activity will be
relatively low, given that the activity of this enzyme is a predictable
indicator of the glycolytic and glycogenolytic pathways
(Bass et al., 1969;
Gregory et al., 2001;
Staudte and Pette, 1972;
Peter et al., 1972). The
present data also suggest a high probability that the type I fiber will have a
relatively high SDH activity, i.e. its potential for oxidative phosphorylation
will be relatively high. It is also apparent that the coordination of protein
systems within a fiber and motor unit extends beyond the three enzyme systems
presently studied (Hallauer and Hastings,
2002; Hamm et al.,
1988; Hood et al.,
2006; Spangenburg and Booth,
2003).
There is a multitude of evidence from other enzyme systems that further
supports the idea of coordinated systems within fiber types. For instance,
Kong et al. (Kong et al.,
1994) reported a 20-fold difference in the amount of the glucose
transporter isoform GLUT4 among the three fiber types in the rabbit TA muscle.
Type I fibers had the highest level followed by type IIa and then IIx. They
went on to note that GLUT4 was highly correlated with the levels of malate
dehydrogenase and hexokinase. Following electrical stimulation of the peroneal
nerve, they reported that GLUT4 and hexokinase increased and remained
coordinated. Meng et al. (Meng et al.,
1993) reported that myoglobin content paralleled oxidative
capacity and was correlated to fiber type in three muscles having widely
varying fiber-type compositions. Type I fibers had a higher content than type
IIa fibers and the lowest amount was found in type IIx fibers. Nishida et al.
(Nishida et al., 1995)
reported the highest levels of myoglobin in slow fibers that had high lactate
dehydrogenase (H-type isoform) activities. The lowest myoglobin content was
found in fast fibers with the highest lactate dehydrogenase (M-type isoform)
activities. Similar correlations were found between carbonic anhydrase III and
myoglobin in three fiber types from human psoas muscle
(Zheng et al., 1992). Each of
these studies indicates that there is not a random expression of proteins in a
muscle fiber but a coordinated program for the expression of protein systems
among a variety of species. The mechanisms by which this coordination of gene
expression within a single fiber and among the fibers of a motor unit can
occur remain unclear. One of the most obvious candidates for this coordination
within a muscle unit is neuronal, with this neural influence exerted
via activity-dependent and -independent means
(Hyatt et al., 2003;
Hyatt et al., 2006;
Roy et al., 1996).
Numerous studies have reported some level of control manifested
via the motoneurons that limits the diversity in the properties among
fibers of a muscle unit. In the case of the cat TA, it is about 25–30%
of the diversity observed among all fibers of the muscle
(Pierotti et al., 1994).
Although the phenotype of a fiber can be modulated by varying the amount
and/or pattern of activation it receives
(Pette and Vrbova, 1999;
Salmons and Sreter, 1976), it
is equally clear that the level of activation is not the only, and perhaps not
even the dominant, control mechanism exerted by the motoneuron
(Edgerton et al., 1996). The
range in motor unit and muscle fiber types observed in muscles for control
animals persists after 6 months of virtual electrical silence
(Pierotti et al., 1991). In
addition, when a change does occur in a pool of silenced motor units, it
occurs at the motor unit level, i.e. some motor units change while others do
not (Zhong et al., 2002).
The mechanisms through which this neuronal control occurs in the absence of
neural activity remain elusive, but it is clear that muscle fiber phenotypes
are not defined solely by the activity levels or patterns of the motoneurons
(Edgerton et al., 1996;
Hyatt et al., 2003;
Hyatt et al., 2006). In
addition, there is clear evidence that when the axon remains intact with the
muscle as in the SI preparation, compared with denervation, the TA muscle
atrophies less and myogenic genes are modulated less severely, thus
illustrating the importance of a non-activity source of neural control
(Hyatt et al., 2003;
Hyatt et al., 2006).
Perspective
The present data clearly show that the biological significance of the
diversity in the biochemical properties of muscle fibers is manifested in (1)
the coordination of gene expression within and among protein systems of a
single fiber, (2) the coordination of this gene expression within a single
fiber with that in all fibers innervated by the same motoneuron, and (3) the
range and combination of gene expression patterns that are formed and
sustained by a pool of motoneurons that innervate a muscle in a way that
matches the physiological demands of a given muscle. The present data also
demonstrate a high level of persistence of the diversity and coordination of
protein systems within and among fibers of a motor unit and across motor units
of the same muscle after 6 months of electrical silence. In general, these
data demonstrate that it is not the diversity in values of any single property
that underlies the significance of muscle fiber phenotypes but the
coordination of the expression of selected combinations of genes. The
mechanisms that control this level of coordination seem to be mediated
neurally as well as non-neurally. In addition, the neurally induced control
can be via neural activity- or non-activity-linked mechanisms.
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Acknowledgments |
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References |
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