From the Program in Proteomics and Bioinformatics, University of Toronto, Toronto, Ontario M5S 3E2, Canada and ¶ the Banting and Best Department of Medical Research, University of Toronto, Toronto, Ontario M5G 1L6, Canada
These authors contributed equally to this work || Postdoctoral research fellow of the Heart and Stroke Foundation of Canada ** Postdoctoral research fellow of the Heart and Stroke Foundation of Ontario. Present address: Protana Inc., 251 Attwell Dr., Toronto, Ontario M9W TH4, Canada
Present address: Dept. of Biochemistry and Molecular Biology, University of Calgary, 3330 Hospital Dr., Calgary, Alberta T2N 4N1, Canada
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ABSTRACT |
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Cultured undifferentiated fibroblast-like mouse and human myoblast cell lines serve as excellent model systems for investigating the complex biochemical adaptations that underlie the formation of functional myocytes. In the absence of mitogenic stimuli, proliferating myoblasts synchronously withdraw from the cell cycle, elongate, adhere, and finally fuse together to form myotubes exhibiting most, if not all, of the principle mechanobiochemical adaptations associated with contractility (1). Molecular genetic studies of this myogenic program have provided fundamental insight into key regulatory events associated with this profound resetting of cell physiology (2). These include the discovery of critical intracellular signaling pathways and their target sequence-specific transcriptional factors that control cascading waves of gene expression in terminally differentiating myoblasts, leading to large scale biological reorganization and the formation of functional myofibrils (3, 4). Nonetheless knowledge of the full range of biochemical adaptations associated with myocyte formation remains incomplete, masking the complexity that is likely to exist in vivo.
Genome scale molecular profiling studies offer a unique opportunity to investigate the molecular hierarchy and biochemical logic that governs muscle cell development and physiology. To this end, several groups have reported the use of DNA-based microarray technology to examine global patterns of transcription in cultured myoblasts during the transition from mitotic cell proliferation to terminal differentiation (57). These studies have provided evidence of significant changes in gene expression patterns during myogenesis. However, because mRNA transcript levels do not always correlate with corresponding cognate protein levels (8), determination of the full spectrum of biochemical alterations associated with skeletal muscle formation might be best ascertained by monitoring alterations in protein abundance and turnover directly (9, 10). Systematic comparison of changes in the proteome composition of mitotic and differentiating myoblasts should also provide insight into the various mechanisms and pathways that underlie the formation of skeletal muscle.
To this end, Tannu and colleagues used two-dimensional (2D)),1 PAGE-based proteomic methods to compare the levels of soluble proteins in mitotic and fully differentiated C2C12 mouse myoblasts (11). Although several notable alterations in protein abundance were uncovered, overall the proteome adaptations detected were surprisingly slight and less dynamic than widely assumed. Given that gel-based methods have limited sensitivity, modest overall dynamic range, and substantive detection biases (12, 13), it is likely that many of the proteome perturbations associated with the muscle differentiation program went undetected, particularly those associated with lower abundance proteins.
Recent advances in alternative gel-free proteome profiling methods combining LC with ultrasensitive tandem MS-based peptide sequencing (LC-MS) offer a complementary and likely more effective experimental approach for examining global changes in protein expression as a function of development (14, 15). Here we report the results of an extensive LC-MS-based shotgun profiling analysis of alterations in protein expression in differentiating C2C12 myoblasts as a function of the myogenic program. Striking changes were detected in the abundance of hundreds of proteins linked to cell adhesion, intracellular signaling, gene expression, metabolism, and muscle contraction, consistent with a substantive and highly dynamic biochemical remodeling of cell function. These data were broadly consistent with the predictions reported in a recent microarray-based study of myogenic gene expression by Tomczak et al. (7) and provide compelling evidence for the involvement of previously overlooked transcription regulators, signaling factors, and adhesion molecules as well as novel uncharacterized proteins in skeletal muscle development and contractility.
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EXPERIMENTAL PROCEDURES |
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Cell Culture and Protein Preparation
Mitotic C2C12 mouse myoblasts (American Type Culture Collection (ATCC), Manassas, VA) were passaged as subconfluent monolayers in Dulbeccos modified Eagles medium (Invitrogen) supplemented with 20% fetal bovine serum, 200 mML-glutamine, 10 units/ml penicillin, and 10 µg/ml streptomycin. Confluent (90%) myoblasts were differentiated into myotubes by culturing the cells in Dulbeccos modified Eagles medium supplemented with 2% horse serum. Crude nuclear extracts of harvested cells were prepared using a commercial kit (Nu-CLEARTM extraction kit, Sigma). Briefly cell pellets were resuspended in 5 volumes of hypotonic lysis buffer (10 mM HEPES, pH 7.9, 1.5 mM MgCl2, 10 mM KCl) and incubated on ice for 15 min. The suspension was centrifuged for 5 min at 420 x g, and the resulting pellet was resuspended in 400 µl of lysis buffer. The cells were disrupted using a glass tissue homogenizer with a type B pestle and centrifuged for 20 min at 1,000 x g. The crude nuclear pellet was resuspended in extraction buffer (20 mM HEPES, pH 7.9, 1.5 mM MgCl2, 0.42 M NaCl, 0.2 mM EDTA, 25% glycerol) and incubated for 30 min with gentle shaking followed by several strokes with a glass pestle. The suspension was centrifuged for 5 min at 21,000 x g with the crude nuclear extract supernatant used for further analysis.
Protein Digestion
Aliquots of extract (150 µg of total protein) were precipitated with 5 volumes of ice-cold acetone overnight at 20 °C. The precipitate was centrifuged for 20 min at 21,000 x g, and the pellet was dissolved in solubilization buffer (8 M urea, 50 mM Tris-HCl, pH 8.5, 2 mM DTT) for 1 h at 37 °C. The soluble proteins were alkylated with 5 mM iodoacetamide, diluted to 4 M urea with 50 mM ammonium bicarbonate, pH 8.5, and then digested overnight with endoproteinase Lys-C at 37 °C. The following day, the samples were further diluted to 2 M urea with 50 mM ammonium bicarbonate, pH 8.5, supplemented with CaCl2 to a final concentration of 1 mM and digested overnight with Poroszyme immobilized trypsin beads at 30 °C with rotation. The resulting peptide mixtures were desalted using solid phase extraction with SPEC-Plus PT C18 cartridges according to the manufacturers instructions and stored at 80 °C until further use. Sample pH was made <3 by adding formic acid prior to LC-MS analysis.
Multidimensional Protein Identification Analysis
The multidimensional protein identification technology (MudPIT) shotgun profiling methodology reported by Washburn et al. (16) was used essentially as described in Kislinger et al. (17). Briefly a Surveyor quaternary HPLC pump (Thermo Finnigan, San Jose, CA) was interfaced with a Finnigan LCQ DECA XP ion trap tandem mass spectrometer. A 150-µm-inner diameter fused silica microcapillary column (Polymicro Technologies, Phoenix, AZ) was pulled using a P-2000 laser puller (Sutter Instruments, Novato, CA) and packed with 10 cm of 5-µm Zorbax Eclipse XDB-C18 (Agilent Technologies, Inc., Mississauga, Ontario, Canada) followed by
6 cm of 5-µm Partisphere strong cation exchange resin (Whatman). Samples were loaded manually onto a separate column using a pressure vessel with the loaded column then connected to the HPLC pump using a polyetheretherketone microcross. Each sample was analyzed via a fully automated 15-cycle data-dependent chromatographic analytical method using a low flow (<300 nl/min) set-up essentially as described previously (17).
Informatics
Over 250,000 uninterpreted product ion mass spectra were sequence-mapped against a locally maintained, minimally redundant data base of human and mouse protein sequences (Swiss-Prot and TrEMBL; downloaded from the European Bioinformatics Institute) using the SEQUEST data base search algorithm (18) running on a multiprocessor computer cluster. The false discovery rate was estimated by also searching against an equivalent number of reversed decoy non-sense protein sequences (17). Putative protein sequence matches were evaluated statistically using STATQUEST, a Perl-based computer algorithm that uses an error estimation model to compute the accuracy of individual predictions to minimize the false discovery rate (17). A stringent threshold cutoff (p value, <0.05), corresponding to a 95% or greater likelihood that a given protein was correctly identified, was used to filter the data sets. The 1,865 high confidence proteins were then subgrouped into specific functional categories based on the Gene Ontology (GO) annotation schema (19) using the Perl-based computer program GOClust (17). 1,487 proteins (80%) were linked to one or more GO terms: 1,138 proteins (61%) mapped to the principle GO category biological process, 1,316 proteins (71%) mapped to the GO category molecular function, whereas 1,184 proteins (64%) mapped to the GO category cellular component (Supplemental Table S1).
Hierarchical Clustering, Data Visualization, and Cluster Evaluation
The total cumulative spectral count recorded from each protein was interpreted as a semiquantitative measure of relative abundance (20). For the time course analysis, relative protein abundance was estimated by calculating the ratio of the natural logarithm of total spectra detected for each protein relative to that detected in the undifferentiated, asynchronously proliferating (day 0) myoblast cell population. Use of the log scale allows negative expression ratios to be more readily visualized as a -fold change rather than fractional (simple ratio) data. It also tightens the spread of the data points, allowing for more subtle local differences to be detected across a broader dynamic range. Hierarchical clustering was carried out using the Cluster 3.0 freeware software package using the correlation distance metric with average linkage selected. To improve data grouping, a nominal low, non-zero cutoff value (0.01) was substituted for blank values in cases where a protein was not detected in a particular sample. The clustered data profiles were visualized in heat map format using the TreeView software package (21).
Statistical enrichment of proteins matching to select functional categories (GO terms) within each of the clusters was assessed using the hypergeometric distribution (22), which reflects the probability (p value) that the intersection of a given protein list with any given annotation term occurs more frequently than would be expected by chance. A Bonferroni amendment factor was used to correct for multihypothesis testing; the p value deemed significant for an individual test was determined by dividing the preliminary value by the number of tests conducted, thereby accounting for spurious significance due to repeat testing over all the categories in the GO data base. A threshold cutoff value of 103 was used as a final selection criterion to highlight promising, biologically interesting clusters.
Proteomic and Microarray Data Set Comparisons
The complete supplemental gene expression data sets from the microarray study of Tomczak et al. (7), which was based on the mouse MG_U74Av2 and MG_U74Cv2 Affymetrix (Santa Clara, CA) GeneChip platforms, were parsed into a relational data base. Cross-comparison of the respective genomic and proteomic profiles was carried out by first cross-referencing the respective cognate gene products by mapping Swiss-Prot/TrEMBL accessions using annotation tables downloaded from the Affymetrix website. The scaled microarray data were then aligned and co-clustered with the proteomic patterns using the Spearman distance metric. To correct for differences in experimental design and slight temporal shifts, the entire microarray data set was normalized by dividing each data point by the value recorded for either day 2 or day 0 according to the nomenclature of Tomczak et al. (7), whereas the day 0 proteomic time point was used as the reference for calculating the ratio of protein expression. The entire set of mapped gene protein pairs with their corresponding data values is presented in Supplement Table S2.
Reverse Transcription (RT)-PCR
Total RNA was extracted from cells using Tripure (Roche Applied Science). Total RNA (100 ng) was used for RT-PCR essentially as described previously (23). cDNAs were amplified using specific primer pairs based on available NCBI sequence data (Supplemental Table S3) and visualized on 1% agarose gels. Between 22 and 32 amplification cycle numbers were used per reaction to ensure linearity of response.
RESULTS
Proteomic Investigation of an in Vitro Model of Myogenesis
A temporally well defined myogenic differentiation program can be triggered selectively in cultured C2C12 myoblasts upon withdrawal of media-derived growth factors and mitogens (23). When switched to differentiation medium (see "Experimental Procedures"), mitotic C2C12 myoblasts rapidly cease proliferating and initiate a synchronously terminal differentiation program (Fig. 1). The cells also exhibit striking morphological changes over the course of 26 days, eventually fusing into mature multinucleated myotubes (i.e. by day 6).
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Approximately 50,000 fragmentation spectra, encompassing >5,000 peptides selected for collision-induced dissociation, were acquired for each protein fraction. The data base search algorithm SEQUEST (18) was used to match these spectra to peptide sequences present in a minimally redundant data base of mouse and human proteins obtained from Swiss-Prot/TrEMBL (25). The probability assessment algorithm STAQUEST (17) was then used to assign a statistical confidence score to each putative candidate identification. In practice, due to the non-linear distribution of data base confidence scores, the majority of the predictions had extremely high likelihoods of being correct. As seen in Supplemental Fig. S1, the majority of data base matches had predicted likelihood scores greater than 99%. As an independent quality measure, the preponderance of incorrect (false positive) matches was empirically calculated by populating the reference data base with an equal number of mock decoy proteins created by inverting the amino acid orientations of the normal Swiss-Prot/TrEMBL protein sequences. Matches to these "reverse" sequences represent spurious false positives because they are not expected to occur naturally. The final proportion of matches mapping to reverse proteins relative to normal (or "forward") proteins provided an objective criterion for estimating the false discovery rate.
Quality filtering of large scale expression data sets represents a trade-off between specificity (precision), which reflects the proportion of correct identifications, and sensitivity (recall), which indicates the bone fide proteomic coverage attained. Receiver-Operator-Characteristic plots are often used to assess the effects of varying classification criteria on classification precision and recall. Although prior knowledge of the correct class labels is not available (because we do not know a priori which proteins are in fact present in the samples), in practice one can estimate this trade-off empirically based on the fraction of data base matches to the forward and reverse sequences after applying various quality filters. The Receiver-Operator-Characteristic-like plot shown in Supplemental Fig. S2 shows the effects of applying confidence filters of different stringency to the time course data sets.
To ensure dependable accuracy, we opted to apply a stringent first pass probability filter (albeit at the expense of reduced detection coverage) corresponding to a minimum of 95% confidence to each candidate peptide match. This stringent filtering resulted in the detection of 1,865 high confidence (p value, <0.05) proteins throughout the myogenic differentiation program. Importantly only
2.5% reverse decoy proteins passed this criteria (data not shown), confirming its reliability. A high level summary of the final data set is shown in Table I, whereas a breakdown of the spectral counts detected per protein per time point is provided in Supplemental Table S4. A more complete description of the search results (including peptide sequence, precursor ion mass and charge, and the search algorithm and preliminary confidence scores) is provided in Supplemental Table S5. Several hundred of these high confidence proteins appear to be muscle lineage-specific as they were not detected in previously reported extensive proteomic analyses of unrelated mouse tissues, such as lung and liver (Ref. 17; data not shown). These included a surprisingly large number (
250) of uncharacterized proteins (i.e. "hypothetical," "unknown," or simply labeled as "Riken" cDNA products).
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In this manner, striking changes in protein function were detected throughout the time course (Fig. 2). A first major phase followed immediately after initial withdrawal of serum (i.e. day 2) and was represented by a sharp but transient increase in levels of intracellular signaling and transcriptional regulatory factors (labeled as cluster A; discussed further below). A second phase, consisting of more gradual waves of expression of cell remodeling factors, such as proteases and intercellular adhesion molecules, followed next (cluster B). By day 10, a marked alteration in overall metabolic capacity and cytoskeletal organization was apparent, including dramatically elevated levels of proteins linked to mitochondrial function, fatty acid oxidation, and muscle contraction (cluster C), consistent with the gross morphological and physiological transitions associated with terminal differentiation and formation of muscle-like myotubes (2729). This phase was also accompanied by commensurate down-regulation of enzymes linked to cell division, protein synthesis, and DNA metabolism (cluster D), coincident with exit from the cell proliferation cycle and cessation of cell growth and division.
For a closer examination of alterations in these core processes, the proteins were sorted based on membership to discrete functional categories (GO terms). Because low spectral counts are more likely to be prone to spurious variance (20), more reliable biological inferences can be drawn by looking for trends across the samples (i.e. functional categories showing altered expression across the differentiation time points). As outlined below, analyses of the resulting subgroups helped to highlight biologically interesting patterns of differential protein expression occurring during progression of the myogenic program.
Protein Expression Patterns in Early Myogenic Development (Dividing Myoblasts)
Proliferating myoblasts were found to preferentially express 257 proteins linked to a range of functions associated with cell proliferation, chromosomal replication, and the mitotic cell cycle, several of which are listed in Table II (see Supplemental Table S6 for complete details). These included many proteins mapping to the GO term nucleus (53 proteins, including CREB-binding protein, DNA polymerase, and the transcription factor Notch 4), cell cycle (eight proteins, including Cullin-3 and the cyclin-dependent kinase 1 (CDK1)), DNA replication (eight proteins, including replication factors MCM4 and DNA primase), and chromosome (six proteins, including high mobility group protein 4, telomeric repeat binding factor 2, and the serine/threonine protein kinase splicing factor PRP4).
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Expression Patterns during Early Stage Induction of the Differentiation Program
Substantive changes in protein levels were detected immediately following induction of the myogenic program (day 2) by withdrawal of serum-derived mitogens, consistent with a major shift from cell division to terminal differentiation. Many of these proteins were detected exclusively at this stage, several of which are listed in Table III. These included marked up-regulation of proteins mapping to select functional categories (see Supplemental Table S7 for complete details), including the GO terms nucleus (55 proteins, such as vitamin D3 receptor, CpG-binding protein, mRNA-capping enzyme, and ATP-dependent chromatin remodeling protein), DNA-dependent regulation of transcription (20 proteins, including the Kruppel-like factor 4, homeobox protein Meis1, nuclear receptor corepressor 1, mothers against decapentaplegic (SMAD) 4, and lamin B receptor), and protein binding (35 proteins, such as neurogenic locus Notch homolog protein 3, syntaxin4, Rab6-interacting protein 2 isoform, myosin 5, and thyroid receptor-interacting protein).
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As seen in Fig. 3, the RT-PCR data correlated well with the proteomic results because in most instances the time points producing the most intense RT-PCR products were largely the same as those for which the most elevated proteomic count was recorded. Consistent findings were observed for SIX1, SIX4, SMAD3, and Notch 3. Nevertheless some lag was observed between the relative gene product abundance as predicted by LC-MS and RT-PCR (e.g. protein and mRNA species). Although we cannot exclude sampling error, these temporal shifts most likely reflect differences between mRNA and protein accumulation as well as post-translational control mechanisms (8, 30).
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Co-clustering of the respective time course profiles revealed good overall concordance in the respective time course patterns (Fig. 4A). 497 (91%) of the gene products showed similar temporal trends in expression (i.e. elevated or depressed levels as a function of terminal differentiation) in response to myogenesis after normalizing the microarray data using either the day 2 (Fig. 4A) or day 0 (Fig. 4B) time points as reference to correct for minor differences in translation lag. The coordinately up-regulated gene products were found to be significantly enriched in GO terms related to muscle function, such as muscle development, calcium ion binding, and muscle contraction. In contrast, a large co-cluster of down-regulated gene products was found to be enriched for GO terms related to cell division, such as cell cycle, DNA replication, and chromosome.
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DISCUSSION
The development of striated muscle depends on a complex series of integrated mechanisms that ultimately reprogram gene expression and drive cellular reorganization (2729). This process is initiated through the regulation of a network of intracellular signaling pathways that impinge on select sequence-specific myogenic transcription factors (1, 3134). Although traditional reductionist methods have helped researchers to elucidate key mechanistic aspects of the myogenic program, the development of global expression profiling methods now offers the opportunity to investigate this multilayered process from a holistic, system-wide perspective.
In this study, we have aimed to provide an as complete and unbiased as possible molecular overview of the biochemical adaptations associated with the formation of functional myocytes by systematically examining the temporal dynamics of protein expression in a C2C12 model system using shotgun sequencing. The most striking changes in protein expression detected in response to terminal differentiation occurred during two distinct physiological transitions. The first phase coincided with a rapid withdrawal of the myoblasts from the cell cycle following mitogen deprivation, concomitant with a shift from active proliferation to the initiation of differentiation. This postmitotic phase was associated with substantive alterations in the levels of a number of critical signaling factors. The second transition consisted of a series of more gradual, but ultimately more profound, perturbations in a broad range of biological systems driving the morphological conversion of single cells into fused, elongated, multinucleated myotubes. This phase included large scale cytoskeletal rearrangements, enhanced intercellular adhesion, and the maturation of both the contractile apparatus and even entire organelles such as peroxisomes and mitochondria.
Exit from the cell cycle occurs through blockade of the CDK mitotic engine (35, 36). Consistent with this expectation, the proliferating myoblast cell population was found to preferentially express high levels of key activators of mitotic division (Table II and Supplemental Table S6), such as CDK1 and Cdc5, without detectable expression of any known CDK inhibitor. On the other hand, the levels of all detectable cell cycle-related proteins decreased markedly following initiation of the differentiation program.
Muscle-specific Transcription Factors
The major developmental pathways directing skeletal muscle formation are governed by the action of two major classes of transcriptional regulatory factors: namely the MyoD family of muscle-regulatory factors and the myocyte-specific enhancer factor 2 (MEF2) family of MADS box transcription factors. Each family member encodes a basic helix-loop-helix domain that directs binding to specific myogenic DNA-regulatory elements, called E-boxes (sequence, CANNTG), in response to appropriate physiological signals (37). Three key members of these families were identified in this study (Supplemental Table S4), including MEF2D, which was up-regulated significantly in the mid stages of myotube formation (days 26), and the MyoD/E-box-associated transcriptional co-activators 12 and 4 (Class A basic helix-loop-helix factors ME1 and ME2). MEF2D is known to regulate expression of numerous skeletal muscle-specific genes positively, including creatine kinase and troponin (38, 39), which were both detected at elevated levels at later time points. Nevertheless we were unable to detect most of the other major myogenic transcription factors, such as myogenin, muscle-regulatory factor 4, Myf5, or MyoD, presumably because these are expressed at low levels below the detection limits of the LC-MS methodology used here. Previous proteomic studies of the myogenic differentiation program using 2D PAGE-based technology have likewise failed to identify these factors (11), indicating an important limitation of proteomic profiling using current instrumentation.
Nonetheless several other developmentally important transcriptional regulators were identified in this analysis (Table IV), including the homeobox proteins SIX1 and SIX4, whose levels were sharply elevated immediately following serum withdrawal, consistent with previous reports (40). Both factors have been implicated in the control of muscle formation through binding to an evolutionary conserved MEF3-binding site upstream of the myogenin promoter and resulting in the activation of myogenin transcription (40), a key step in skeletal muscle development. The MEF3 site is also present in other skeletal muscle-specific genes, including the promoters of genes encoding troponin C (41), creatine kinase (42), and the glycolytic enzyme aldolase A (43, 44), all of which were detected as being up-regulated during later stages of myogenic differentiation. Likewise myocyte nuclear factor (also known as Forkhead box protein K1) was also detected immediately following serum deprivation (Supplemental Table S4), consistent with an early role in myogenic differentiation (45, 46). Myocyte nuclear factor encodes one of the first winged-helix Hepatocyte Nuclear Factor 3/Forkhead family of transcription factors identified as a key regulator of the myogenic program (46, 47). It acts by binding to the CCAC box sequence motif found in the promoter region of the myoglobin gene and thereby activating transcription (45).
Intracellular Mediators of Myogenic Signaling
Although a crude nuclear preparation (expected to contain nuclear, contractile, and mitochondrial proteins) was prepared, we were also able to detect a diverse set of proteins involved in signal transduction (see Supplemental Table S4 for details). In particular, key members of the Ras superfamily of signaling proteins, which broadcast a host of signals from activated receptor tyrosine kinases at the cell surface through to the nucleus (48, 49), were identified particularly during the initial stages of cell differentiation. For instance, elevated levels of both R-Ras and the Ras-related protein Rab-33B were detected immediately following serum withdrawal. Likewise several critical downstream targets of Ras were induced at early stages of differentiation, including the mitogen-activated protein kinases MAPKK-1 and -2, extracellular signal-regulated kinase-1, and the MAPKK-1-interacting protein 1 as well as AF-6, a putative signaling protein. In contrast, the Ras GTPase-activating-like protein IQGAP1, which is proposed to participate in the reorganization of the actin cytoskeleton during cellular remodeling (50), was found to be expressed throughout all stages of myogenesis. Taken together, these data indicate that significant differences in the accumulation of core components of Ras-MAPK-mediated signaling occur during the myogenic process.
In contrast, components of at least two major signaling pathways known to inhibit myogenic differentiation were found to be down-regulated in response to initiation of myogenic program (Tables II and III). The first major pathway involves Notch signaling, which inhibits myogenesis by interfering with MEF2 activation (5153). For instance, Notch 3 and 4 were detected preferentially in the proliferating myoblasts (day 2), consistent with the results of previous microarray studies (6). The second pathway involves the SMAD family of signaling proteins (54, 55). The transcription factors SMAD3, -4, and -5 were all detected preferentially early during myogenesis (i.e. in proliferating myoblasts and early stage myotubes), again consistent with a proliferative function.
Several other notable signaling factors (Supplemental Table S4) were preferentially detected in the late stage myotubes, including ArgBP2, DCAMKL1, and Copine III. ArgBP2 is a novel member of the Abelson (Arg/Abl) protein-tyrosine kinase-binding proteins and is predicted to be a substrate of Arg and v-Abl (56). ArgBP2 localizes to the Z-disc in cardiac muscle where it likely influences contractility and elastic properties of cardiac sarcomeres in response to activation of Abelson-linked signaling cascades (56). DCAMKL1 is a serine/threonine protein kinase implicated in Ca2+ signaling and has been shown to control neuronal cell migration in the developing brain (57). On the other hand, Copine III, a putative protein kinase detected in both proliferating and early differentiating cells, has been linked to the regulation of membrane trafficking (58).
Sarcomeric Organization and Muscle Contraction
Myofibrils, which form the bulk of muscle mass, are composed of tandem arrays of sarcomeres, the core structural unit of dynamically interacting chains of actin and myosin filaments responsible for muscle contraction (59). The development and maturation of the contractile apparatus requires a parallel increase in calcium-dependent cycling proteins to handle the rapid fluxes in Ca2+ associated with muscle contractility (60). Consistent with this, myotube formation was associated with elevated levels of calcium transporters, including the Ca2+ release channel (ryanodine receptor), voltage-gated L-type calcium channel, and the sarco(endo)plasmic reticulum calcium ATPase. As expected, a large set of other proteins linked to skeletal muscle excitation/contraction (such as the cytoskeletal factors actin, myosin, troponin, nebulin, titin, desmin, and -actinin) were likewise enriched in late stage differentiating cells (Table IV and Supplemental Table S8).
The ATP requirements of contracting muscle are extremely high both for ATP-dependent Ca2+ handling and for ATP-dependent myosin cross-bridge formation. As a result of this marked demand, skeletal muscle metabolizes large amounts of glucose, fatty acids, and amino acids to fuel contraction (61). Consistent with this, late stage myotubes were greatly enriched in enzymes linked to basic metabolism, including key rate-limiting energy-producing enzymes such as pyruvate kinase, creatine kinase, ATP synthase, acyl-coenzyme A oxidases, and 3-hydroxyacyl-CoA dehydrogenases (Table VI and Supplemental Table S10).
Cytoskeleton and Extracellular Matrix Reorganization
The muscle cytoskeleton consists of a complex network of filaments and tubules that transmit mechanical and chemical stimuli within and between adjacent myocytes. This dynamic structure also contributes to cell stability during the contraction cycle by anchoring subcellular structures such as mitochondria, nuclei, and myofibrils. The stabilizing mechanotransducer action is supported by membrane-associated proteins, in particular dystrophin, which binds to intracellular actin and extracellular laminin through the dystroglycan complex (62). As expected, myogenesis was associated with the induction of a vast array of cytoskeletal factors, such as desmin and nebulin, as well as muscle-specific components of the dystrophin complex, such as dystroglycan and sarcoglycan.
Reorganization of the contractile apparatus was paralleled by an increased abundance of connective tissue proteins in the developing myocytes, which provide physical stabilization during the extreme tensile forces generated during contraction. These included numerous collagen isoforms, integrins (
5,
v,
7, ß1, and ß5), laminins (
-2,
-5, ß-1, and
-1), and fibronectin. In addition, matrix metalloproteases, including glycoproteins, and adhesion factors, such as
- and ß-sarcoglycan, were expressed preferentially during cell differentiation or were detected exclusively in terminally differentiated myotubes. Numerous other adhesion or structural proteins, including nidogen and dystroglycan, were also enriched in later stages of differentiation.
Protein Functional Annotation
In this study, we detected expression of 25 uncharacterized Riken cDNAs and
180 putative hypothetical proteins. Comparison of their expression profiles with those of previously studied proteins can provide clues as to the possible functions or roles of these gene products. In particular, linkage to a specific cluster exhibiting significant functional enrichment suggests that several of these uncharacterized proteins are likely to participate in processes important to proper myogenesis, including the control of mRNA transcription, chromatin modification, and/or cell cycle progression.
Of course, the observed clusters of functionally related proteins highlighted in this report reflect only a portion of all the proteins identified in this study. In addition to suggesting a potential role for novel gene products in skeletal muscle development, our results implicate a number of well characterized proteins that have not been linked previously to muscle cell differentiation. For example, two proteins involved in RNA synthesis (ATP-dependent RNA helicase (DEAD box protein) and UMP/CMP kinase) as well as a protein involved in endocytosis (intersectin-1) were found to be differentially expressed during myogenic differentiation, yet these proteins had not been associated with muscle development previously. A systematic, hypothesis-driven analysis of similarly identified proteins based on reasonable interpretations of their expression characteristics seems likely to bear fruit. Hence our proteomic data can be viewed as a resource for targeted follow-up studies centered on one or more biochemical pathways of particular interest.
Comparison with Microarray-based Investigations of Myogenic Gene Transcription
In the past year, several studies have reported the use of DNA microarray technology to probe changes in gene expression in C2C12 cells coincident with the formation of mature myocytes (57). Gratifyingly the results of the most extensive gene expression study published to date (7) were found to correlate quite well with the proteomic data reported here with relatively few substantive inconsistencies at least in terms of overall temporal trends. This concordance was somewhat surprising given the general reports in the field and validates the general conclusions of the two studies and the respective experimental platforms, further encouraging follow-up investigations. Although gene expression profiling studies generally achieved a more extensive coverage (at least 2,895 mRNA transcripts were predicted to be expressed throughout skeletal myogenesis in Ref. 7), our study provides complementary evidence for possible post-translational controls that may serve to further refine the biochemical transitions associated with myogenesis. Moreover, although certain gross incongruities in the observed patterns of protein and transcript levels are likely to have arisen due to technical limitations such as biased detection or other artifacts associated with LC-MS and microarray analyses, many may in fact represent bona fide, biologically meaningful differences, stemming from differential regulation of translation and/or mRNA or protein stability as has also been suggested previously in the literature (63).
Comparison with 2D PAGE Profiling of Myogenesis
A recent study (11) reported the use of 2D PAGE followed by silver staining and quantitative imaging to examine changes in the levels of 2,000 protein spots throughout the myogenic differentiation. Although the vast majority did not exhibit any detectable differences in terms of relative abundance,
100 predicted to be differentially expressed were identified, including some 26 phosphorylated variants. We determined that there were 39 proteins in total that were detected by Tannu et al. (11) as well as this study; 29 of these proteins (74%) showed virtually identical findings (see Supplemental Table S11 for details), whereas only 10 (26%) proteins (annexin V, protein-disulfide isomerase, transcription intermediary factor 1-ß, histone acetyltransferase type B, guanine nucleotide-binding protein ß subunit, 26 S proteasome-regulatory subunit, Lasp-1, 75-kDa glucose-regulated protein, secreted protein acidic and rich in cysteine (SPARC), and myosin light chain 1) showed significant differences in expression patterns between our two studies. Nevertheless despite the limited dynamic range afforded by SDS-PAGE, the substantial agreement between these two independent proteomic data sets further validates the main conclusions of this new study and the reliability of gel-free protein expression profiling as a means to investigate fundamental aspects of muscle cell biology. These modest discrepancies most likely arose due to technical differences in tissue culture, protein extraction, or sample preparation. Although gel-based profiling techniques offer certain advantages (64), our results indicates that gel-free shotgun methods offer fundamentally enhanced proteomic coverage (14).
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FOOTNOTES |
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Published, MCP Papers in Press, April 11, 2005, DOI 10.1074/mcp.M400182-MCP200
1 The abbreviations used are: 2D, two-dimensional; LC-MS, LC coupled to tandem MS-based peptide sequencing; MudPIT, multidimensional protein identification technology; GO, Gene Ontology; RT, reverse transcription; CREB, cAMP-response element-binding protein; CDK, cyclin-dependent kinase; MEF, myocyte-specific enhancer factor; MAPK, mitogen-activated protein kinase; MAPKK, MAPK kinase.
* This work was partially supported by Canadian Institutes of Health Research Grant MOP 49493, the Neuromuscular Research Partnership, and a grant from the Muscular Dystrophy Association (United States) (to D. H. M.); by a developmental grant from the Muscular Dystrophy Association (United States) (to A. O. G.); and by grants from the Natural Sciences and Engineering Research Council of Canada, the Protein Engineering Network Centre of Excellence (PENCE), and funds from the Ontario Genome Institute and Genome Canada (to A. E.)
S The on-line version of this manuscript (available at http://www.mcponline.org) contains supplemental material.
To whom correspondence should be addressed: CH Best Inst., 112 College St., Rm. 402, Toronto, Ontario M5G 1L6, Canada. Tel.: 416-946-7281; Fax: 416-978-8528; E-mail: andrew.emili{at}utoronto.ca
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