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J Appl Physiol 98: 1396-1406, 2005. First published December 17, 2004; doi:10.1152/japplphysiol.01055.2004
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Transcriptional profile of a myotube starvation model of atrophy

Eric J. Stevenson, Alan Koncarevic, Paul G. Giresi, Robert W. Jackman, and Susan C. Kandarian

Department of Health Sciences, Boston University, Boston, Massachusetts

Submitted 24 September 2004 ; accepted in final form 12 December 2004


   ABSTRACT
TOP
 ABSTRACT
METHODS
 RESULTS
 DISCUSSION
 GRANTS
 REFERENCES
 
Skeletal muscle wasting is a pervasive phenomenon that can result from a wide range of pathological conditions as well as from habitual muscular inactivity. The present work describes a cell-culture condition that induces significant atrophy in skeletal muscle C2C12 myotubes. The failure to replenish differentiation media in mature myotubes leads to rapid atrophy (53% in diameter), which is referred to here as starvation. Affymetrix microarrays were used to develop a transcriptional profile of control (fed) vs. atrophied (nonfed) myotubes. Myotube starvation was characterized by an upregulation of genes involved in translational inhibition, amino acid biosynthesis and transport, and cell cycle arrest/apoptosis, among others. Downregulated genes included several structural and regulatory elements of the extracellular matrix as well as several elements of Wnt/frizzled and TGF-{beta} signaling pathways. Interestingly, the characteristic transcriptional upregulation of the ubiquitin-proteasome system, calpains, and cathepsins known to occur in multiple in vivo models of atrophy were not seen during myotube starvation. With the exception of the downregulation of extracellular matrix genes, serine protease inhibitor genes, and the upregulation of the translation initiation factor PHAS-I, this model of atrophy in cell culture has a transcriptional profile quite distinct from any study published to date with atrophy in whole muscle. These data show that, although the gross morphology of atrophied muscle fibers may be similar in whole muscle vs. myotube culture, the processes by which this phenotype is achieved differ markedly.

skeletal muscle; microarray; C2C12

MUSCLE ATROPHY DUE TO INACTIVITY, fasting, and disease states, such as cancer, diabetes, and sepsis, results in a disruption of the normal balance between protein synthesis and degradation (16, 17, 20, 44, 46, 47). These diverse physiological and pathological conditions seem to trigger muscle atrophy through distinct mechanisms. For example, decreased muscular activity triggers atrophy via mechanisms related to decreased tension, whereas elevated levels of glucocorticoids and/or cytokines do so during illness and fasting (13, 50). However, the idea has been proposed that with various types of muscle atrophy, such as those induced by fasting and other disease states, there are subsets of genes whose differential expression is common to these types of atrophy (19), suggesting that this process shares common mechanisms regardless of the triggering event. Even with disuse atrophy due to muscle unloading (41) or immobilization (34), several subsets of genes showed differential expression that was similar to that seen with illness models (19), and recent models put all kinds of atrophy inducers in a common pathway (37, 38). Thus it appears that, at least in rodent models of disuse, fasting, and disease, there are some similar mechanisms underlying the atrophy process. The genes most often referred to that suggest a common transcription program in the various forms of atrophy are those involved in ubiquitin-proteasome protein degradation.

To study the molecular details of muscle atrophy, several cell-culture models have been developed. For the most part, these have involved the treatment of cultured myotubes with exogenous cytokines (e.g., TNF-{alpha}) or glucocorticoids (i.e., dexamethasone). These compounds have been used because they are thought to be involved in triggering atrophy in various types of cachexia, and in fact they do induce increased protein degradation in cultured myotubes similar to that seen during cachexia and fasting in vivo (22–24, 39, 48). In recent studies, dexamethasone treatment and/or starvation of cultured cells were used as model systems for defining the molecular details of whole muscle atrophy (37, 42). Indeed, both dexamethasone- and PBS (short-term starvation)-treated cells showed activation of at least one mRNA that is recently considered a marker of atrophy, the ubiquitin protein ligase atrogin-1/MAFbx (37). These cell-culture models have been said to "mimic" atrophy in vivo because they have in common at least a few protein and mRNA changes (38). It is not clear, however, to what extent the cell-culture models recapitulate the mRNA profile of atrophy in whole muscle.

In the course of identifying a suitable cell culture model of atrophy that could be used to work on the molecular details of disuse atrophy, we developed a slightly different myotube atrophy model. Regular refeeding of differentiated muscle cells is necessary for the maintenance of viability and myotube size. We demonstrate here that the failure to replenish media of mature myotubes in culture leads to rapid thinning. The first objective of this work was to determine how similar this myotube atrophy model was to disuse atrophy, which we have previously detailed (41); that is, we wanted to determine whether we could use this model to study whole muscle atrophy, particularly since other work suggested the concept of a common atrophy transcriptional program. To identify the common differentially expressed genes in myotube atrophy and disuse muscle atrophy (41), the transcriptional profiles were compared using Affymetrix microarrays. Second, we identified differentially expressed genes in common with myotube atrophy and fasted mouse muscle from published data (18) by comparing their transcriptional profiles. A final objective was to describe the atrophied myotube phenotype and thereby better understand the mechanism of this type of myotube atrophy.

Although there were large differences in gene expression when control and starved cells were compared, the transcriptional profile was distinct from any type of atrophy published to date (2, 18, 19, 41, 51). In addition, the increase in atrogin expression, which is now commonly used as a marker of atrophy (38), did not occur in the atrophied myotubes in this study. It therefore appears that the cellular mechanisms underlying atrophy in this cell culture model differ markedly from those in the in vivo models because only a few genes were differentially expressed in common. Starvation of myotubes in culture revealed a distinctive phenotype, not a suitable model to study signaling pathways of whole muscle atrophy conditions, yet indicating that the overt atrophy phenotype reflects a great many differential gene expressions and is not simply attributable to a particular set of genes. It will remain important for each atrophy condition under study to be evaluated independently both with respect to the particular genes responsible for the changes and to the control mechanisms that would be therapeutic targets against atrophy in that condition.


   METHODS
TOP
 ABSTRACT
 METHODS
RESULTS
 DISCUSSION
 GRANTS
 REFERENCES
 
Cell Culture

C2C12 mouse myoblasts (American Type Culture Collection, Manassas, VA) were maintained in growth medium (DMEM supplemented with 10% fetal bovine serum, Invitrogen Corporation, Carlsbad, CA) in 5% CO2. Cells were plated at a density of ~50% in 100-mm dishes or in 6-well plates. When cells reached ~90% confluency 24 h later, they were switched to differentiation media (DMEM containing 2% horse serum; GIBCO, Invitrogen Corporation, Carlsbad, CA), which was subsequently changed every 48 h. All cells were allowed to differentiate for 4 days with feeding every 2 days (days 0, 2, and 4). Beginning at day 4, control cells were refed every 48 h (standard refeeding time for C2C12 myotubes is 24–48 h), whereas experimental cells were not refed. After an additional 4 days (8 days postdifferentiation), cells were harvested and total RNA was isolated from control and "starved" (nonrefed cells) (Fig. 1).



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  		Fig. 1. Experimental design. Myotubes differentiated in DMEM containing 2% horse serum for 4 days were split into 2 groups. In the control group, differentiation media was removed and replenished every other day. In the "starved" group, cells were not fed after day 0 (i.e., day 4 of differentiation). After 4 days (8 days after the induction of differentiation), cells were harvested and total RNA was isolated from each plate (n = 4 plates in each group). After 4 days without media and serum replenishment, myotubes become visibly thinner. Myotubes are visualized using an anti-myosin primary antibody.

 
Immunocytochemistry and Myotube Diameter Measurements

On day 8 of differentiation, control and starved cells grown in 6-well plates were fixed using 1.5% formalin for 20 min, permeabilized using 1% Triton for 20 min, and incubated with mouse monoclonal anti-myosin MF20 primary antibody (Hybridoma Bank, University of Iowa), followed by incubation with rabbit anti-mouse fluoroscein-conjugated Alexa Fluor 488 secondary antibody (Molecular Probes). The myotubes were visualized at x20 magnification through a FITC-HYQ filter using an inverted light microscope (Nikon) and captured with a Spot RT camera and Spot Software (Diagnostic Instruments). Fiber diameter was measured from randomly selected microscope fields from three different wells (35 mm) of control and three different wells of starved myotubes (6 wells total). At least three diameters were measured per myotube, and at least 150 myotubes were measured per well using MetaMorph Imaging software (Universal Imaging). A Student's t-test was performed to assess differences in fiber diameter between the two groups.

Total RNA Isolation and cRNA Synthesis

Total RNA was isolated from four different 100-mm plates of control myotubes and four different 100-mm plates of starved myotubes using the RNAqeous-4PCR kit (Ambion) for isolation of DNA-free RNA. RNA quality was measured as a function of the ratio of absorbance at 260 and 280 nm. Furthermore, all RNA samples were analyzed using agarose gel electrophoresis and stained to check for integrity of 18S and 28S RNA. Reverse transcription, second-strand synthesis, labeled cRNA preparation, and hybridization to the mouse expression set 430A GeneChip were all performed at Partners Healthcare Gene Array Technology Center (Brigham and Women's Hospital, Boston, MA) using the recommended protocol by Affymetrix. Hybridization conditions have been detailed elsewhere (26).

Expression Profiling

The Affymetrix mouse expression set 430A GeneChip, which contains a total of 22,690 probe sets, was used in this analysis. Eight separate microarrays were used (4 for control samples and 4 for starved samples). The detail of the chip design is described elsewhere (26). Briefly, each gene probe is represented by ~16–20 "perfect match" 25-mer oligonucleotides paired with an equal number of "mismatched" oligonucleotides that have a single nucleotide difference in a central position of the 25 mer. Comparison of the hybridization patterns of the perfect match and mismatched pairs allow for the elimination of nonspecific hybridization signals. Each of the 16–20 probe pairs is designed to span sequences in the 3' region of the gene and is complementary to the cRNA targets prepared from tissue samples.

Image analysis was performed using Affymetrix Microarray Suite 5.0 (MAS 5.0) as previously described (25). Briefly, the MAS 5.0 statistical algorithm provides an intensity value (transcript abundance) that indicates whether a transcript is detected above the background level for each gene based on the hybridization performance of the perfect match and mismatched. MAS 5.0, uses a one-sided Wilcoxon's signed rank test to determine the detection call [present (P < 0.04), marginal (0.04 = P < 0.06), or absent (P ≥ 0.06)], and a one-step Tukey's biweight estimate was used to calculate overall signal intensity.

Microarray data from this project have been submitted to National Center for Biotechnology Information's Gene Expression Omnibus according to the Microarray Information About Microarray Experiments standards. Data for the sample series GSE1776 [NCBI GEO] can be accessed at http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?aco=GSE1776.

Data Analysis and Statistical Filtering

Step 1: normalization and noise reduction.   Signal intensities on each chip were normalized with respect to each other using a linear scaling method in which signal intensities for each probe set are multiplied by a normalization term (6). Normalization of signal intensities across all chips is necessary to eliminate the effects of chip-to-chip variations in target preparation, hybridization, and scanning.

Probe sets that receive absent calls are usually associated with low-intensity expression values and/or a high level of intraprobe set variability. Low-intensity levels may represent the specific binding of a transcript expressed at low levels but may also be the result of factors that influence background and noise. To reduce the contribution of noise-based error in subsequent statistical analysis, each probe set that did not receive at least one present call across all eight chips was removed. This reduced the dataset from 22,690 to 13,216 probe sets (i.e., gene products).

Step 2: significance testing.   A t-test was used to determine whether each of the 13,216 probe sets showed significant differences in expression when comparing control (n = 4 plates of cells) and starved (n = 4 plates of cells) myotubes. The 2,506 probe sets passed the t-test (P < 0.05).

Step 3: multiple test correction.   When standard statistical methods are used to analyze microarray data, it is important to consider the effect of the number of tests being performed on type I error (or false positives). In this dataset, the t-test was performed 13,216 times (see step 2). Testing multiple hypotheses in these situations can effectively increase type I errors, so it has become common to use correction methods to reduce this type of error. We have therefore applied the false discovery rate method of Benjamini and Hochberg (3, 36) to each test (13, 216 tests), which calculates a new rank-adjusted P-value threshold. The observed P values (Pobs) from the original test statistic were ranked based on significance, with the most significant (smallest P value) result being first and the least significant (largest P value) being last. A new set of thresholds (Padj) were then determined based on rank:

where Poriginal was the original threshold of 0.05 and N is the total number of comparisons (in this case N = 13,216). So the most significant probe set would be evaluated against a Padj of 3.78 x 10–6 (i.e., 0.05·1/13,216). These new rank-adjusted thresholds (Padj) (from 13,216 comparisons or probe sets) were then compared with the ranked Pobs and all probe sets where Padj ≥ Pobs was considered significant (527 probe sets). The new Padj thresholds were compared with the Pobs for the experimental effect (i.e., starvation; 527 probe sets passed this test). The largest significant P value was 0.002, and therefore the false-positive rate was 0.2%.

This also allowed us to calculate a false discovery rate, which is the ratio of the number of false discoveries [those that would be expected by chance (NFD) divided by the number of total discoveries (NTD)].

where FDR is the false discovery rate. NFD was the largest P value (0.002) multiplied by the number of comparisons (N = 13,216). The number of probe sets found to be significantly different using the t-test on all present genes was 2,506 (NTD). This resulted in a false discovery rate of 0.0105 (1.1%).

Step 4: fold change cutoff.   The next step in filtering the data set was to eliminate those probe sets that did not have a minimum fold change of ±1.5. Although the multiple test correction indicated that changes even smaller than 1.5-fold could be considered statistically significant with a false-positive rate of 0.2%, by applying the ±1.5-fold cutoff, the data set was reduced from 527 to 397 probe sets (representing 315 unique mRNAs). The 1.5-fold cutoff was selected because, for this study, we were interested in analysis of the medium to larger changes in gene expression and because this is the fold cutoff suggested by the GeneChip manufacturer to further reduce false positives (26). For comparisons of gene expression changes between myotube starvation and whole muscle models of atrophy (unloading and starvation), we used these results from the t-test with multiple test correction and the 1.5-fold cutoff.

Marker Analysis

Gene Cluster 2.0 (www.broad.mit.edu/cancer/software/) was used to identify the genes that best discriminate between the control and starved phenotypes. A "feature selection" (i.e., gene selection) method was used that assigns a discriminatory strength value to each of the probes (that had a presence call) based on its signal-to-noise ratio (9). The signal-to-noise ratio is a modified t statistic that favors probe sets with large differences in group means and low within-group variability. Therefore, a higher score (more positive or more negative) would indicate a larger difference in mean expression values between groups and/or low within-group variance. In this case, we used marker analysis to identify the top 50 upregulated and the top 50 downregulated genes whose expression was best at distinguishing between the control and starved phenotype. Thus these genes tend to be among those that represent the most significant markers of the atrophy process. All 100 of these genes also passed the t-test. This does not indicate that the other genes in the t-test list (the other 215 genes) are unimportant in myotube atrophy.


   RESULTS
TOP
 ABSTRACT
 METHODS
 RESULTS
DISCUSSION
 GRANTS
 REFERENCES
 
Starvation of Myotubes in Culture

In this study, we examined the effects of nonrefeeding (which we generally refer to as starvation) on skeletal muscle myotubes in cell culture. Cells examined after 4 days of starvation were dramatically thinner (53%) than the control, regularly fed myotubes (Fig. 2). This represents true atrophy because there was no difference in the diameter of the control myotubes at experimental day 0 (4 days of differentiation) compared with experimental day 4 (day 8 of differentiation; data not shown).



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  		Fig. 2. Decrease in myotube diameter during starvation. A: representative field of fed myotubes visualized using an anti-myosin primary. B: antibody representative field of starved myotubes. On day 8 of differentiation, fiber diameter was measured from randomly selected fields from 3 different wells each of control and starved myotubes. C: there was a 53% decrease in myotube diameter due to starvation. At least 3 diameters were measured per myotube, and at least 150 myotubes were measured per well. *Statistically different from control cells (P < 0.05).

 
Microarray Analysis of In Vitro Myotube Starvation

The step-by-step statistical analysis outlined in the METHODS section was used to determine that 397 probes sets show significant changes in gene expression during starvation. Of the 397 probe sets, 315 correspond to unique gene products when redundant probes are removed. A listing of these genes can be found at http://www.bu.edu/kandarian/data/data2.htm. Pairwise intragroup comparisons (control vs. control and starved vs. starved) of all probe sets on the mouse 430A chip showed a mean correlation coefficient of 0.99 ± 0.0012 (mean ± SE), demonstrating the strong homogeneity of each cell population as a whole. The average correlation coefficient of intergroup comparisons (control vs. starved) was 0.97 ± 0.0008, suggesting differential expression compared with the within-group correlations, which were higher. Correlation coefficients for the intra- and intergroup pairwise comparisons are shown in supplemental Table I.

Marker Analysis

Marker (i.e., gene) selection was performed on the 13,216 probe sets that passed the initial step in our statistical filter (see METHODS). The discriminatory strength of each probe set was determined by scoring with the signal-to-noise ratio (9), a modified t statistic that penalizes probe sets with high intragroup variability. In this case, we used marker analysis to identify the top 50 downregulated (Fig. 3A) and the top 50 upregulated genes (Fig. 3B) whose expression best distinguished the starved phenotype in myotubes. This provided a snapshot of the largest and least variable molecular changes that occur during starvation. The results are shown in the form of a heat map because it is useful to visualize the distinctive differences in gene expression between control and starved myotubes. The order of the listing of the genes in Fig. 3 is by the ranking of scores from the marker analysis; the best performing gene is listed first, and the 50th performing gene is listed last for both up- and downregulated gene lists. The fold change is also given in this figure, but this value only roughly matched the gene ranking because expression variability is also taken into consideration in determining the ranking order (i.e., the marker analysis score, not shown). The power of this approach in identifying sets of discriminatory genes is demonstrated by the magnitude of change and the uniform expression levels for each gene within each group, as would be expected, since this is a cell line. In some cases, multiple probe sets exist on the GeneChip for the same gene. The accuracy of this approach in identifying strong markers of the phenotype is further exemplified by the fact that multiple probe sets for the same gene were all identified as being the best markers of the starvation model. This can be seen more easily in Tables 1 and 2, which shows the list of marker genes in order of functional category. Each of the 100 differentially expressed genes identified using marker analysis also passed the t-test, multiple test correction, and fold change cutoff (i.e., they are among the 315 gene products mentioned above).



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  		Fig. 3. Heat map from marker analysis displaying the relative expression values for the top 50 downregulated (A) and top 50 upregulated (B) genes due to starvation. A: top 50 downregulated genes that best distinguish control and starved myotubes (i.e., downregulated with starvation). For each probe set, the relative expression of each sample is represented by the number of standard deviations from the mean (z score). The color intensity of each block, either red (greater than mean value) or blue (lower than mean value), represents the magnitude of difference from the mean. The genes are ranked in order of their score (1st to 50th) from the marker analysis. The fold change (FC) of each probe set was determined by using the raw expression data (note: the FC number is different from the z score, which is reflected by the color intensity). B: top 50 upregulated genes that best distinguish control and starved myotubes (i.e., upregulated with starvation).

 

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  		Table 1. Top 50 downregulated marker genes listed by functional category

 

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  		Table 2. Top 50 upregulated marker genes listed by functional category

 
Figure 4 shows the raw data for all eight samples for 2 of the 100 genes selected using the marker selection process. The top performing downregulated gene, parathyroid hormone receptor 1, and the 49th best performing upregulated gene, small chemokine (C-C motif) ligand 11, are plotted for comparison. There is a greater magnitude of change and less variability of within-group samples for parathyroid hormone receptor 1 than for chemokine (C-C motif) ligand 11, and this explains its better score in the marker analysis. However, both genes were significantly changed by myotube starvation and are considered gene expression markers of this process.



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  		Fig. 4. Example of genes selected by marker analysis. Normalized intensity values for parathyroid hormone receptor 1 (Pthr1; A) and small chemokine (C-C motif) ligand 11 (Ccl11; B) are plotted for each sample. Samples c1–c4 are control samples and s1–s4 are starved samples. Pthr1 is the top-scoring downregulated gene with starvation (see Fig. 3). Ccl11 is ranked 49th in the genes upregulated with starvation. High-scoring genes like Pthr1 show significant differences among subjects and also demonstrate low within-group variability. A gene with a lower score, Ccl11, shows greater variability among starved samples, but significant differences still exist between the 2 group means.

 

   DISCUSSION
TOP
 ABSTRACT
 METHODS
 RESULTS
 DISCUSSION
GRANTS
 REFERENCES
 
Marker Genes that are Downregulated During Starvation

Extracellular matrix.   Structural and regulatory genes of the extracellular matrix and cytoskeleton show an overall decrease in expression during starvation (Fig. 3A and Table 1). Because muscles undergo major structural reorganization during atrophy, this was not unexpected. In this regard, the analysis also shows that cytochrome 450 member 57, metalloprotease Adamts4, and serine (or cysteine) protease inhibitor clade E member are three downregulated genes involved in mediating matrix structure or stress response.

Two matricellular genes (5), thrombospondin 1 and tenascin, are also downregulated in starved myocyte cultures. Thrombospondin 1 has previously been shown to serve as an attachment factor for C2C12 myoblasts (1). Tenascin expression has been shown to be dependent on the Rho family of G proteins (27). The RhoA protein is required for myotube differentiation (45); therefore, a drop in tenascin expression, if due to changes in Rho, could be a sign of other changes in myotube differentiation.

Growth factor signaling.   Several genes involved in the regulation of growth and differentiation (40, 52) are highly expressed in control cells and show decreased expression during myotube starvation. Genes involved in the regulation of insulin-like growth factor, Wnt/frizzled and TGF-{beta} (follistatin-like) signaling are downregulated in starved cells. IGF binding protein (IGFBP) 4 is another gene showing decreased expression during starvation. This is an interesting finding, since IGBFP4 is thought to have an inhibitory effect on IGF-I- and IGF-II-stimulated proliferation and differentiation (7). The dramatic decrease in IGFBP4 may be a compensatory response in which all possible IGF molecules are released to maintain cellular integrity.

Marker Genes that are Upregulated During Starvation

Amino acid transport and biosynthesis.   Two solute carriers that transport amino acids, solute carrier family 1 (glutamate/neutral amino acid transporter), member 4, and solute carrier family 7 (cationic amino acid transporter, y + system), member 5, and several genes involved in asparagine, serine, methionine, and proline biosynthesis were more highly expressed in starved myotubes, each being upregulated from 2.8- to 6.8-fold during starvation (Fig. 3B and Table 2). This may indicate that, during starvation, these gene products are upregulated in an attempt to synthesize necessary amino acids.

Cell cycle arrest and apoptosis.   Starvation is also associated with an increase in several genes with roles regulating the cell cycle or apoptosis. The upregulation of DNA damage-inducible transcript 3 (GADD153, 2.7-fold) and growth arrest specific 5 (2.7-fold) is consistent with stress-induced cell cycle arrest (8, 32). Growth differentiation factor 15 is a member of the TGF-{beta} family that has been shown to inhibit apoptosis in some situations (43), but its role in myotubes has not been determined. The induced in fatty liver dystrophy 2 gene (average 6.4-fold) and activating transcription factors-3 and -5 (increased 7.6- and 3.1-fold, respectively) are transcription factors that are upregulated. Each is thought to play a role in apoptosis. Induced in fatty liver, dystrophy 2 is a protein with an unknown function encoded by a gene activated in neuronal cells in cytotoxic conditions (e.g., disruption of calcium homeostasis, trophic factor deprivation) (31). Activating transcription factors-3 and -5 are induced by a wide range of stress stimuli and are thought to play an anti-apoptotic role under certain conditions (12, 30). Interestingly, evidence has been presented that, induced in fatty liver, dystrophy 2 can interact with activating transcription factors to inhibit their ability to activate stress-induced gene transcriptional activation (31).

Growth factor signaling.   Starved cells show marked increases in FGF21 (5.6-fold) and IGF-2 (3.1-fold) transcript levels. Both of theses genes play a role in regulating myoblast proliferation and differentiation (28). However, their role in starved myotubes is not clear. Starvation is also associated with the induction of small chemokine (C-C motif) ligand 11, raising the possibility that cytokine signaling is playing a role in the protein loss that is seen in this model.

Comparison to Atrophy in Whole Muscle Models

Because many studies have been using cell culture models to mimic atrophy in vivo (see introduction), we compared the transcriptional profile of the present model of myotube atrophy to unloading atrophy in rats (41) and fasting atrophy in mice (18). There is also a study showing many similarities in whole muscle atrophy due to fasting, cancer, diabetes, and kidney disease (19). Interestingly, there are several subsets of genes that are similarly regulated with both illness and disuse atrophy. For this comparison of myotube atrophy with whole muscle atrophy, we used the 315 unique genes identified as being significantly changed using the t-test, multiple test correction, and the 1.5-fold cutoff (http://www.bu.edu/kandarian/data/data2.htm). The similarities in gene expression changes in the starved cells vs. either muscle unloading or starvation models of atrophy are listed in Fig. 5. However, because there were many more genes that showed differences in expression in the whole muscle models of atrophy compared with myotube starvation, we have restricted the discussion of these genes to those that seem most relevant to mechanisms by which protein is lost. These include genes involved in the regulation of protein turnover, amino acid metabolism, and some regulatory genes (Fig. 6).



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  		Fig. 5. Changes in gene expression with myotube starvation (MS) that are similar to that seen in whole muscle with unloading (U) or fasting (F). See text for whole muscle data from published studies. Green box indicates gene was upregulated, red box indicates gene was downregulated, and white box indicates no change in gene expression. This figure is a complete list of the similaries in differential gene expression in myotube starvation vs. either of the in vivo models of atrophy. LO-like, lysyl oxidase like; MMP, matris metalloprotease; TIMP, tissue inhibitor of metalloprotease.

 


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  		Fig. 6. Changes in gene expression with myotube starvation (MS) that are different from changes in whole muscle during unloading (U) and fasting (F). See text for whole muscle data from published studies. Green box indicates gene was upregulated, red box indicates gene was downregulated, and white box indicates no change in gene expression. This figure is only a partial list of the differences in genes expression among the myotube vs. whole muscle models. IGFBP, IGF binding protein.

 
Regulation of protein turnover.   Disuse atrophy in rodents is marked by an initial decrease in protein synthesis rate that is responsible for the majority of protein loss during the first few days of atrophy (47). Although most of these changes can be attributed to posttranslational mechanisms affecting translation factors, a transcriptional component to this regulation has also been uncovered. For example, our work with disuse atrophy and other models of atrophy (fasting) have shown an early increase in transcript levels of translation initiation factor 4E binding protein 1 (PHAS-I) (4, 18, 41). This is also seen in our cell culture model of atrophy by starvation. When unphosphorylated, PHAS-I is a translational repressor that inhibits eIF-4E-dependent mRNA translation. The upregulation of PHAS-I appears to be a potential marker for forms of atrophy that involve the global repression of translational capacity. Beyond the upregulation of PHAS-I, differential expression of genes involved in protein turnover during myotube starvation does not resemble that of unloading or fasting in animals, which involves a concomitant upregulation of initiation factors and several ribosomal proteins (Fig. 6).

Protein chaperones play several cellular functions: guiding the folding of nascent polypeptide chains, thus facilitating translation, and protecting proteins from cellular stress. Three different protein chaperones were differentially expressed with myotube starvation. Heat shock protein E1 and EROL-like are two chaperones that are upregulated during cell starvation. Heat shock protein E1 is a mitochondrial chaperone (10), whereas the EROL-like protein is an endoplasmic reticulum membrane protein involved in oxidation-induced protein folding (33). Interestingly, one of the most striking features of the atrophy process during unloading involves the global downregulation of protein chaperones that play a role in protecting membrane, cytosolic, and cytoskeletal proteins. Only two chaperones are differentially regulated during fasting; one is upregulated and the other is downregulated. Thus the global downregulation of protein chaperones appears to be a phenomenon specific to unloading-induced atrophy and may play more of a gene-regulatory or organelle-specific protection.

Disuse atrophy is also associated with molecular changes consistent with the development of reactive oxygen species and redox damage to proteins. Data from unloaded skeletal muscle showed marked differential expression of mRNAs involved in regulating levels of glutathione and an upregulation of mRNAs encoding enzymes that play a chemoprotective role against oxidative stress (41). Jagoe et al. (18) have also shown decreased expression of glutathione reductase and catalase during fasting-induced atrophy. The activation of several metallothionein 1 subunits, another marker of oxidative stress, is also induced in both unloading- and fasting-induced atrophy. Beyond the transcriptional downregulation of glutathione peroxidase 1, the transcriptional profile of myotube starvation exhibited no evidence of an oxidative stress response similar to that seen in other models of atrophy.

Perhaps the most striking difference between starvation of myotubes and whole muscle models of atrophy is the complete absence of the activation of genes involved in intracellular protein degradation. Atrophy induced by a variety of stimuli (unloading, fasting, cachexia, diabetes, etc.) is associated with increased expression and activity of three different proteolytic systems: the ubiquitin-proteasome system, the Ca2+-dependent calpains, and the lysosomal cathepsins (16, 17, 20, 41, 44). The extent of this activation is demonstrated in the unloading and fasting data in Fig. 6. This was not, however, the case during myotube starvation. In fact, no calpains or cathepsins appear in our filtered data set. Most importantly, with regard to the ubiquitin proteasome system, no proteasome subunits were differentially expressed, and in fact one ubiquitin-protein ligase in the data set was downregulated (Grail).

The idea that atrophy may be in part driven by amino acid limitation is supported by the fact that several genes involved in amino acid biosynthesis are strongly upregulated during myotube starvation. This could be an attempt by the cells to synthesize the amino acids necessary for metabolism, growth, and maintenance of cellular structures. Disuse atrophy, on the other hand, involves the slight activation of a different and smaller subset of genes that play a role in amino acid synthesis. But unloading atrophy is characterized with a more robust activation of genes involved in amino acid catabolism and is therefore distinct, in this regard, to myotube starvation (Fig. 6).

Extracellular Matrix Remodeling

One common aspect of most forms of atrophy involves significant extracellular matrix remodeling. Indeed, the global downregulation of many structural elements of the extracellular matrix occurs in all three of the models discussed here (Fig. 5). Genes that are similarly downregulated during atrophy in all three models include several collagen isoforms, fibronection, and fibrillin. Lysyl oxidase and lysyl oxidase-like are extracellular copper enzymes that initiate cross-linking of collagens, and elastin is also downregulated but only with unloading and myotube starvation. Decorin is a component of connective tissue that binds type I collagen fibrils and plays a role in matrix assembly. The expression of this gene is upregulated during both unloading and myotube starvation. Although decorin may play a role in the remodeling process that occurs during atrophy, it has also been shown to have growth-suppressive properties (29).

Galectin-1 is another multifunctional extracellular protein that plays both structural and regulatory roles. Galectin-1 is upregulated in unloading and fasting-induced atrophy, and we have also shown that this gene is highly expressed in aging human muscle (unpublished data). The galectins are thought to be involved in mediating cell-cell and cell-matrix interactions but have also been implicated as a regulator of inflammation (14). Galectin-1 is anti-inflammatory in its ability to activate apoptosis in infiltrating T cells, thereby limiting T-cell-induced cellular damage and destruction (14). Probes for Galectin-1 do exist on the mouse GeneChip, but their expression was not shown to change during starvation in myotubes, indicating this may be a whole muscle-specific hallmark of atrophy (Fig. 5).

The dissolution of the extracellular matrix and basement membrane are dependent on the activities of several extracellular protease and protease inhibitor cascades. Several matrix metalloproteases and metalloprotease inhibitors are differentially expressed in all three atrophy models (Fig. 5). The models exhibit a similar decrease in metalloprotease-100 and strong decreases in tissue inhibitor of metalloprotease 1. Other than these genes, in general, other metalloproteases (i.e., membrane-inserted metalloprotease-14) are upregulated with disuse atrophy, whereas they are downregulated during starvation-induced myotube atrophy (Fig. 6). All three models of atrophy show a similar reduction in the expression of several members of the serpin superfamily of serine protease inhibitors (clades) (Fig. 5). These genes play a role in a variety of biological processes, including complement activation and collagen maturation. Serpins have also demonstrated the ability to inhibit the degradation of components of the extracellular matrix. This raises the possibility that decreased expression of these genes is necessary to enable the matrix remodeling seen in a variety of models of muscle atrophy. The role of these proteins in skeletal muscle has not previously been explored, but the fact that their downregulation appears to be characteristic of several types of atrophy makes them an attractive target for future studies. Extracellular metallocarboxypeptidases are membrane-anchored proteases that are thought to play a role in the processing of intercellular peptide messengers and secreted pathway messengers. Carboxypeptidase D is upregulated in muscles atrophied by disuse, whereas carboxypeptidase E is downregulated during both myotube starvation and fasting in vivo. The role of these proteases in muscle are not well understood, but they may play a role in intracellular protein trafficking or in the maturation of secreted peptides or growth factors.

Regulatory Genes

With regulatory genes, there were no distinct patterns similar in all three models. Each model shows differential expression of genes involved in cell adhesion, GTPase- or G-protein-mediated signaling, growth factor signaling, stress, or cytokine signaling, as well as genes involved in inducing cell cycle arrest or apoptosis (for a partial list, see Fig. 6). The signaling genes that make myotube starvation unique have been discussed above. The only gene that is similarly changed in all three models of atrophy is the follistatin-like protein (Fig. 5). This protein is similar to follistatin, an activin-binding protein. The role of this gene in muscle is not known, but it is thought to modulate the activity of follistatin itself (35). Interestingly, the follistatin mRNA has been shown to be highly induced during both unloading-induced atrophy (41) and age-related atrophy in human skeletal muscle (49), but it was not induced in the starved cells or in whole muscle starvation. This is unexpected since follistatin physically interacts with myostatin to inhibit its binding and growth, preventing activity (15). Overexpression of follistatin in mice results in hypertrophy similar to that seen with myostatin knockouts (21).

In conclusion, given the recent assumptions about the similarity of cell culture models of atrophy to whole muscle atrophy (11, 37, 38), it was important to measure how similar the transcriptional profiles were in at least one model of myotube atrophy. The cessation of feeding myotubes in cell culture leads to a significant reduction in the diameter of the cells and a marked change in the transcriptional profile evidenced by the large magnitude of up- and downregulation of genes as visualized by the heat maps. However, the results presented here suggest that starvation atrophy in myotubes involves a mechanism for protein loss different from that of other forms of atrophy in whole muscle. It is particularly interesting that myotube starvation does not lead to upregulation of genes involved in the proteolytic systems as is seen with inactivity, fasting-, and illness-induced atrophy. Because both myotube starvation and fasting in whole muscle involve nutrient limitation, the fact that their transcriptional profiles are distinct was unexpected. Fasting, however, involves an increase in circulating glucocorticoids, which stimulate the release of muscle protein. This stimulus is lacking in the cell culture model and could be the reason why there is not a similar increase in the expression of intracellular proteases (i.e., cathepsins and ubiquitin-proteasome components). It appears that myotube atrophy in this model may be mediated by decreased protein synthesis rather than the acceleration of proteolysis seen with whole animal models of atrophy. It is also possible that proteases involved in muscle protein breakdown are activated at the protein rather than the mRNA level. To make such conclusions, additional studies are necessary to better characterize the time course of molecular changes that occur during myotube atrophy in culture.


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This work was supported by grants from the National Institutes of Health (R21 AG-19754 and R01 AR-41705) and the National Space Biomedical Research Institute (MA00207).


   FOOTNOTES
 

Address for reprint requests and other correspondence: S. Kandarian, Dept. of Health Sciences, Boston Univ., 635 Commonwealth Ave., Boston, MA 02215 (E-mail: skandar{at}bu.edu)

The costs of publication of this article were defrayed in part by the payment of page charges. The article must therefore be hereby marked "advertisement" in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.


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