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On Marathons and Sprints: An Integrated Quantitative Proteomics and Transcriptomics Analysis of Differences Between Slow and Fast Muscle Fibers

2011; Elsevier BV; Volume: 11; Issue: 6 Linguagem: Inglês

10.1074/mcp.m111.010801

1535-9484

Hannes C. A. Drexler, Aaron Ruhs, Anne Konzer, Luca Mendler, Mark Bruckskotten, Mario Looso, Stefan Günther, Thomas Boettger, Marcus Krüger, Thomas Braun,

Muscle Physiology and Disorders

Skeletal muscle tissue contains slow as well as fast twitch muscle fibers that possess different metabolic and contractile properties. Although the distribution of individual proteins in fast and slow fibers has been investigated extensively, a comprehensive proteomic analysis, which is key for any systems biology approach to muscle tissues, is missing. Here, we compared the global protein levels and gene expression profiles of the predominantly slow soleus and fast extensor digitorum longus muscles using the principle of in vivo stable isotope labeling with amino acids based on a fully lysine-6 labeled SILAC-mouse. We identified 551 proteins with significant quantitative differences between slow soleus and fast extensor digitorum longus fibers out of >2000 quantified proteins, which greatly extends the repertoire of proteins differentially regulated between both muscle types. Most of the differentially regulated proteins mediate cellular contraction, ion homeostasis, glycolysis, and oxidation, which reflect the major functional differences between both muscle types. Comparison of proteomics and transcriptomics data uncovered the existence of fiber-type specific posttranscriptional regulatory mechanisms resulting in differential accumulation of Myosin-8 and α-protein kinase 3 proteins and mRNAs among others. Phosphoproteome analysis of soleus and extensor digitorum longus muscles identified 2573 phosphosites on 973 proteins including 1040 novel phosphosites. The in vivo stable isotope labeling with amino acids-mouse approach used in our study provides a comprehensive view into the protein networks that direct fiber-type specific functions and allows a detailed dissection of the molecular composition of slow and fast muscle tissues with unprecedented resolution. Skeletal muscle tissue contains slow as well as fast twitch muscle fibers that possess different metabolic and contractile properties. Although the distribution of individual proteins in fast and slow fibers has been investigated extensively, a comprehensive proteomic analysis, which is key for any systems biology approach to muscle tissues, is missing. Here, we compared the global protein levels and gene expression profiles of the predominantly slow soleus and fast extensor digitorum longus muscles using the principle of in vivo stable isotope labeling with amino acids based on a fully lysine-6 labeled SILAC-mouse. We identified 551 proteins with significant quantitative differences between slow soleus and fast extensor digitorum longus fibers out of >2000 quantified proteins, which greatly extends the repertoire of proteins differentially regulated between both muscle types. Most of the differentially regulated proteins mediate cellular contraction, ion homeostasis, glycolysis, and oxidation, which reflect the major functional differences between both muscle types. Comparison of proteomics and transcriptomics data uncovered the existence of fiber-type specific posttranscriptional regulatory mechanisms resulting in differential accumulation of Myosin-8 and α-protein kinase 3 proteins and mRNAs among others. Phosphoproteome analysis of soleus and extensor digitorum longus muscles identified 2573 phosphosites on 973 proteins including 1040 novel phosphosites. The in vivo stable isotope labeling with amino acids-mouse approach used in our study provides a comprehensive view into the protein networks that direct fiber-type specific functions and allows a detailed dissection of the molecular composition of slow and fast muscle tissues with unprecedented resolution. Skeletal muscles contain different types of fibers, which are responsible for specific biological properties and functions of individual muscles. Muscle fibers have been classified into slow type I and fast type II fibers mainly based on myofibrillar ATP staining and immunohistochemistry using specific antibodies (1Schiaffino S. Reggiani C. Molecular diversity of myofibrillar proteins: gene regulation and functional significance.Physiol. Rev. 1996; 76: 371-423Crossref PubMed Scopus (1268) Google Scholar). Slow type I fibers show a red tint, contain high numbers of mitochondria, and their energy supply is mainly based on oxidative metabolism. These features enable slow fibers to execute long lasting contractions, which are essential for the maintenance of body posture. The primary function of type II fibers is the rapid contraction of muscles. Fast fibers are divided into three additional subclasses: Type IIb and IIx (also known as IId) are glycolytic fibers, whereas type IIa fibers are more comparable to oxidative type I fibers (2Booth F.W. Thomason D.B. Molecular and cellular adaptation of muscle in response to exercise: perspectives of various models.Physiol. Rev. 1991; 71: 541-585Crossref PubMed Scopus (557) Google Scholar). Type II fibers, which mainly derive their energy from glycolysis, are thus more susceptible to fatigue compared with Type I fibers. Muscle fibers have also been classified based on the expression of different isoforms of myosin heavy chain (MyHC) proteins. Myosins are the major contractile proteins and their activation by ATP and Ca2+ ions results in shortening of muscle fibers. For example, slow type I fibers express MyHCIβ and the three fast fiber types express MyHCIIa, MyHCIIb, and MyHCIIx (3Pette D. Staron R.S. Myosin isoforms, muscle fiber types, and transitions.Microsc. Res. Tech. 2000; 50: 500-509Crossref PubMed Scopus (631) Google Scholar), respectively. Although the MyHC-based classification is used most often, several other marker proteins for slow and fast muscles have been described. For example cardiac troponin C (Tnnc1), a regulatory Ca2+ binding protein, is expressed in slow and cardiac muscle tissue, whereas Troponin C/STNC (Tnnc2) is predominant in fast type II fibers (4Dhoot G.K. Perry S.V. Distribution of polymorphic forms of troponin components and tropomyosin in skeletal muscle.Nature. 1979; 278: 714-718Crossref PubMed Scopus (165) Google Scholar). Likewise, the calcium-ATPase pumps SERCA1 and 2, which are required for re-uptake of calcium into the sarcoplasmatic reticulum after contraction and for subsequent muscle relaxation (5MacLennan D.H. Toyofuku T. Lytton J. Structure-function relationships in sarcoplasmic or endoplasmic reticulum type Ca2+ pumps.Ann. N.Y. Acad. Sci. 1992; 671: 1-10Crossref PubMed Scopus (54) Google Scholar) are also expressed differentially in slow and fast twitch fibers. SERCA1 is more abundant in fast twitch fibers, whereas SERCA2 is overrepresented in slow muscle fibers (6Delbono O. Meissner G. Sarcoplasmic reticulum Ca2+ release in rat slow- and fast-twitch muscles.J. Membr. Biol. 1996; 151: 123-130Crossref PubMed Scopus (63) Google Scholar). Another example of an unequal protein distribution between fast and slow fibers is the catalytic A-subunit of the calcium-dependent serine-threonine phosphatase calcineurin, which shows predominant expression in fast fibers (7Swoap S.J. Hunter R.B. Stevenson E.J. Felton H.M. Kansagra N.V. Lang J.M. Esser K.A. Kandarian S.C. The calcineurin-NFAT pathway and muscle fiber-type gene expression.Am. J. Physiol. Cell Physiol. 2000; 279: C915-C924Crossref PubMed Google Scholar). Activation of calcineurin-dependent pathways plays an important role for muscle hypertrophy and fiber transition (8Chin E.R. Olson E.N. Richardson J.A. Yang Q. Humphries C. Shelton J.M. Wu H. Zhu W. Bassel-Duby R. Williams R.S. A calcineurin-dependent transcriptional pathway controls skeletal muscle fiber type.Genes Dev. 1998; 12: 2499-2509Crossref PubMed Scopus (835) Google Scholar) in response to physical exercise, aging, or metabolic diseases such as diabetes (9Teran-Garcia M. Rankinen T. Koza R.A. Rao D.C. Bouchard C. Endurance training-induced changes in insulin sensitivity and gene expression.Am. J. Physiol. Endocrinol Metab. 2005; 288: E1168-E1178Crossref PubMed Scopus (88) Google Scholar, 10Handschin C. Chin S. Li P. Liu F. Maratos-Flier E. Lebrasseur N.K. Yan Z. Spiegelman B.M. Skeletal muscle fiber-type switching, exercise intolerance, and myopathy in PGC-1alpha muscle-specific knock-out animals.J. Biol. Chem. 2007; 282: 30014-30021Abstract Full Text Full Text PDF PubMed Scopus (483) Google Scholar). Our current knowledge of fiber type specific protein expression has been established primarily by traditional biochemical and immunohistochemical techniques. More recently, several cDNA-microarray based studies providing a more systematic and unbiased characterization of muscle tissues has helped to improve our understanding of muscle physiology and the mechanisms of fiber transition (11Noguchi S. Tsukahara T. Fujita M. Kurokawa R. Tachikawa M. Toda T. Tsujimoto A. Arahata K. Nishino I. cDNA microarray analysis of individual Duchenne muscular dystrophy patients.Hum. Mol. Genet. 2003; 12: 595-600Crossref PubMed Scopus (65) Google Scholar, 12Stevenson E.J. Giresi P.G. Koncarevic A. Kandarian S.C. Global analysis of gene expression patterns during disuse atrophy in rat skeletal muscle.J. Physiol. 2003; 551: 33-48Crossref PubMed Scopus (217) Google Scholar). However, transcriptional profiles do not necessarily correlate with the steady-state levels of corresponding proteins and fail to give insight into signaling pathways such as are relayed e.g. by protein kinase cascades. So far, most studies aiming at in-depth characterization of the skeletal or heart muscle proteomes have chosen an approach based on two-dimensional gel electrophoresis (13Isfort R.J. Wang F. Greis K.D. Sun Y. Keough T.W. Bodine S.C. Anderson N.L. Proteomic analysis of rat soleus and tibialis anterior muscle following immobilization.J. Chromatogr. B Analyt. Technol. Biomed. Life Sci. 2002; 769: 323-332Crossref PubMed Scopus (36) Google Scholar, 14Gelfi C. Viganò A. De Palma S. Ripamonti M. Begum S. Cerretelli P. Wait R. 2-D protein maps of rat gastrocnemius and soleus muscles: a tool for muscle plasticity assessment.Proteomics. 2006; 6: 321-340Crossref PubMed Scopus (55) Google Scholar, 15Okumura N. Hashida-Okumura A. Kita K. Matsubae M. Matsubara T. Takao T. Nagai K. Proteomic analysis of slow- and fast-twitch skeletal muscles.Proteomics. 2005; 5: 2896-2906Crossref PubMed Scopus (99) Google Scholar), which yielded a limited number of reliably identified proteins. Recent advances in mass spectrometry based proteomics, however, offer new options allowing identification and quantification of thousands of proteins (16Aebersold R. Mann M. Mass spectrometry-based proteomics.Nature. 2003; 422: 198-207Crossref PubMed Scopus (5585) Google Scholar, 17de Godoy L.M. Olsen J.V. Cox J. Nielsen M.L. Hubner N.C. Fröhlich F. Walther T.C. Mann M. Comprehensive mass-spectrometry-based proteome quantification of haploid versus diploid yeast.Nature. 2008; 455: 1251-1254Crossref PubMed Scopus (737) Google Scholar, 18Dephoure N. Zhou C. Villén J. Beausoleil S.A. Bakalarski C.E. Elledge S.J. Gygi S.P. A quantitative atlas of mitotic phosphorylation.Proc. Natl. Acad. Sci. U.S.A. 2008; 105: 10762-10767Crossref PubMed Scopus (1251) Google Scholar). These new techniques have already been exploited in a few studies to characterize differences in protein abundance between different muscle tissues, including limb and extraocular muscles using one dimensional-gel or isoelectric focusing fractionation in combination with high mass accuracy liquid chromatography-tandem MS (LC-MS/MS) analysis (19Fraterman S. Zeiger U. Khurana T.S. Rubinstein N.A. Wilm M. Combination of peptide OFFGEL fractionation and label-free quantitation facilitated proteomics profiling of extraocular muscle.Proteomics. 2007; 7: 3404-3416Crossref PubMed Scopus (42) Google Scholar, 20Kislinger T. Gramolini A.O. Pan Y. Rahman K. MacLennan D.H. Emili A. Proteome dynamics during C2C12 myoblast differentiation.Mol. Cell. Proteomics. 2005; 4: 887-901Abstract Full Text Full Text PDF PubMed Scopus (111) Google Scholar). Here we report a large-scale quantitative analysis to compare the proteomes and transcriptomes of slow soleus and fast extensor digitorum longus (EDL) 1The abbreviations used are:MyHCmyosin heavy chainGOGene Ontology 1The abbreviations used are:MyHCmyosin heavy chainGOGene Ontology muscles of the mouse. Our analysis is based on the use of 13C6-lysine-labeled mice ("SILAC-mouse"), which provides an internal protein standard. We quantified >2100 proteins in slow and fast muscles, of which ∼25% showed a differential expression pattern. Comparison of proteomics data to mRNA expression profiles obtained from the same samples revealed the existence of specific post-transcriptional regulatory mechanisms in slow and fast muscle fibers although the majority of proteins and mRNA molecules showed a similar distribution. Furthermore, we identified 1040 novel class 1 phosphorylation sites indicating a widespread phosphorylation of sarcomeric proteins in skeletal muscle cells. All data were implemented into the newly developed Quantimus database and are readily accessible. myosin heavy chain Gene Ontology myosin heavy chain Gene Ontology Mouse diet substituted for 13C6-lysine was obtained from Silantes (Martinsried, Germany). Mice were generated in house from a C57Bl/6 colony. All chemicals used for tissue extraction, digests, and liquid chromatography were purchased from Sigma - Aldrich and Carl Roth GmbH (Karlsruhe, Germany), except for Lysyl endopeptidase (R), which was obtained from WAKO GmbH (Neuss, Germany). Mice fully labeled with 13C6-Lysine were generated as described (21Krüger M. Moser M. Ussar S. Thievessen I. Luber C.A. Forner F. Schmidt S. Zanivan S. Fässler R. Mann M. SILAC mouse for quantitative proteomics uncovers kindlin-3 as an essential factor for red blood cell function.Cell. 2008; 134: 353-364Abstract Full Text Full Text PDF PubMed Scopus (547) Google Scholar), except that food pellets containing the heavy lysine were purchased from a commercial source (Silantes, Martinsried, Germany). Labeling efficiency was >96% in the F2 generation of mice maintained on a heavy lysine substituted diet as described previously. Ten-week-old individuals of the F2 generation were used for all experiments. The incorporation rate and labeling efficiency of heavy SILAC labeled mice was monitored in each experiment by assessing the average SILAC ratio on a subset of proteins from muscle tissue. Mice were sacrificed by ketanest injection. The thorax was opened immediately after respiratory arrest before the heart was perfused with PBS via the left ventricle and the right ventricle was cut open for drainage. Successful perfusion and blood cell removal was assessed by observing the color change from red to grayish-white of blood rich organs such as lung and liver. Soleus and Extensor digitorum muscles were dissected from the hindlimbs of the animals, washed in PBS, and snap frozen in liquid nitrogen (nheavy = 2, nlight = 2). Muscles from heavy and light SILAC-labeled animals (8.5–13 mg wet weight) were mechanically homogenized in 250 μl of ice cold modified RIPA buffer containing 1% Nonidet P-40, 0.1% sodium deoxycholate, 50 mm Tris-HCl pH 7.5, 150 mm NaCl, and 1 mm EDTA supplemented with complete protease inhibitor mixture (Roche) using an Ultraturrax disperser (IKA, Staufen, Germany) and incubated for 5 min on ice to extract proteins. Alternatively, we used SDS lysis buffer (4% SDS, 100 mm Tris/HCl pH 7.6) to extract muscle fibers. To remove debris lysates were centrifuged at 19,000 × g for 10 min. Supernatants were collected and protein content was determined using detergent compatible Lowry protein assay (Bio-Rad DC). Fifty to 80 μg of a 1:1 mixture of heavy and light muscle RIPA extracts were then separated by gel electrophoresis on precast 4–12% NuPAGE gradient gels (Invitrogen, Carlsbad, CA) and stained with the Colloidal Blue Staining Kit (Invitrogen). Each lane was cut into 15 evenly sized gel pieces and processed for GeLC-MS/MS. Briefly, proteins within gel pieces were subjected to reduction and alkylation, followed by endopeptidase Lys C cleavage. Peptides were then extracted as described (22Shevchenko A. Tomas H. Havlis J. Olsen J.V. Mann M. In-gel digestion for mass spectrometric characterization of proteins and proteomes.Nat. Protoc. 2006; 1: 2856-2860Crossref PubMed Scopus (3531) Google Scholar), desalted, and concentrated by Stage Tips (23Rappsilber J. Mann M. Ishihama Y. Protocol for micro-purification, enrichment, pre-fractionation and storage of peptides for proteomics using StageTips.Nat. Protoc. 2007; 2: 1896-1906Crossref PubMed Scopus (2570) Google Scholar). SDS lysates were subjected to the FASP protocol followed by Offgel separation as described in (24Wiśniewski J.R. Zougman A. Nagaraj N. Mann M. Universal sample preparation method for proteome analysis.Nat. Methods. 2009; 6: 359-362Crossref PubMed Scopus (5043) Google Scholar). For phosphopeptide enrichment we used a combination of (FASP) and strong-cation chromatography followed by titanium dioxide enrichment (25Olsen J.V. Blagoev B. Gnad F. Macek B. Kumar C. Mortensen P. Mann M. Global, in vivo, and site-specific phosphorylation dynamics in signaling networks.Cell. 2006; 127: 635-648Abstract Full Text Full Text PDF PubMed Scopus (2810) Google Scholar). Each sample, representing the peptide content of one gel piece was analyzed by nano-Reversed Phase Chromatography using an Agilent 1100 nanoflow system that was online coupled via in house packed fused silica capillary column emitters (length 15 cm; ID 75 μm; resin ReproSil-Pur C18-AQ, 3 μm) and a nanoelectrospray source (Proxeon) to an LTQ Orbitrap XL mass spectrometer (Thermo Scientific). Peptides were eluted from the C18 column by applying a linear gradient from 5–35% buffer B (80% acetonitrile, 0.5% acetic acid) over 150 min. The mass spectrometer was operated in the data-dependent mode, collecting collision induced MS/MS spectra from the five most intense peaks in the MS (LTQ-FT full scans from m/z 300 to m/z 1800; resolution r = 60,000; LTQ isolation and fragmentation at a target value of 10000). For the identification of phosphopeptides, an LTQ-Orbitrap Velos mass spectrometer was used and MS/MS spectra were generated by higher C-trap dissociation. Briefly, 30,000 ions were accumulated in the c-trap and MS/MS spectra were detected in the orbitrap at a resolution of 7500 (26Olsen J.V. Schwartz J.C. Griep-Raming J. Nielsen M.L. Damoc E. Denisov E. Lange O. Remes P. Taylor D. Splendore M. Wouters E.R. Senko M. Makarov A. Mann M. Horning S. A dual pressure linear ion trap Orbitrap instrument with very high sequencing speed.Mol. Cell. Proteomics. 2009; 8: 2759-2769Abstract Full Text Full Text PDF PubMed Scopus (376) Google Scholar). Raw data files were then processed by MaxQuant software (Version 1.0.014.10) in conjunction with Mascot database searches (Version 2.2) (27Cox J. Mann M. MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification.Nat. Biotechnol. 2008; 26: 1367-1372Crossref PubMed Scopus (9154) Google Scholar). All data were searched against the International Protein Index sequence database (mouse IPI, version 3.54) with 56,149 entries. The database was extended with commonly observed contaminants and concatenated with reversed versions of all sequences. The parameter settings were: LysC as digesting enzyme, a maximum of two missed cleavages, a minimum of six amino acids, carbamidomethylation at cysteine residues as fixed and oxidation at methionine residues as variable modifications. For the phosphopeptide analysis we set phosphorylation of serine, threonine, and tyrosine as variable modification. The false discovery rate was set to 1% at the peptide and protein level. All peptides and phosphopeptides with their SILAC ratios were uploaded to the Quantimus database. The database is based on an Apache Server and MySQL (Version 5). SILAC labeling with 13C6-lysine was specified and accounted for within MaxQuant. The maximum allowed mass deviation was 10ppm for MS and 0.5 Da for MS/MS scans. Only proteins identified with one uniquely assigned peptide to the corresponding protein were included. Detected proteins from IPI 3.54 mouse database and Affymetrix transcript IDs were used for a Gene Ontology (GO) term analysis. Term annotations were derived from uniprot. GO annotatable proteins were used for a hypergeometric test to reveal overexpressed terms. We defined GO terms as overexpressed, having a Benjamini and Hochberg corrected p value smaller than 0.05. Calculations were done by the Cytoscape plugin Bingo. We tested each data set (Soleus and EDL) on mRNA and protein level for overexpression at GO domains cellular component, molecular function, and biological process. Separated lists are provided in an additional table (supplemental Table S1). Tissues were dissected from PBS-perfused animals, and total RNA was isolated using the TRIzol method (Invitrogen). RNA quality was checked on the Agilent 2100 Bioanalyzer using the RNA 6000 Nano Kit. For mRNA expression analysis, the Affymetrix GeneChip Mouse Genome 1.0 Array was employed with the respective 1-cycle target labeling protocol. Data were analyzed by the RMA algorithm using the Affymetrix Expression Console. An unpaired t test was performed with log2-transformed data to identify significantly differentially expressed transcripts, and FC was calculated using DNAStar ArrayStar 3.0 software. For accurate protein quantification we used the stable isotope labeling of amino acid in cell culture approach (SILAC), which is based on the metabolic incorporation of non-radioactive amino acids in which carbon and/or nitrogen atoms are substituted by heavy isotopes (i.e. 13C6-lysine) (28Ong S.E. Blagoev B. Kratchmarova I. Kristensen D.B. Steen H. Pandey A. Mann M. Stable isotope labeling by amino acids in cell culture, SILAC, as a simple and accurate approach to expression proteomics.Mol. Cell. Proteomics. 2002; 1: 376-386Abstract Full Text Full Text PDF PubMed Scopus (4569) Google Scholar). Recently, we extended this technique to in vivo conditions in which a mouse is labeled with the 13C6-lysine isotope (21Krüger M. Moser M. Ussar S. Thievessen I. Luber C.A. Forner F. Schmidt S. Zanivan S. Fässler R. Mann M. SILAC mouse for quantitative proteomics uncovers kindlin-3 as an essential factor for red blood cell function.Cell. 2008; 134: 353-364Abstract Full Text Full Text PDF PubMed Scopus (547) Google Scholar). By feeding mice over two generations with a 13C6 lysine diet, virtually all natural 12C6 containing lysine amino acids are replaced by the heavy amino acid. To identify and quantify proteins that are either predominantly found in fast and slow twitching muscle fibers or common to both muscle types we isolated fast (EDL muscle) and slow (soleus muscle) fibers from the lower leg of the mouse hind limb. In contrast to rats, where the soleus muscle consists mainly of type I fibers, soleus muscles in mice contain an equal amount of type I and type IIa/IIx fibers (29Matsuura T. Li Y. Giacobino J.P. Fu F.H. Huard J. Skeletal muscle fiber type conversion during the repair of mouse soleus: potential implications for muscle healing after injury.J. Orthop. Res. 2007; 25: 1534-1540Crossref PubMed Scopus (25) Google Scholar). Despite these restrictions we choose the soleus muscle as the slow muscle reference for our proteomic analysis because it contains the highest amount of type I fibers and the remaining type II fibers are mostly oxidative. Based on peak intensities (see methods), equal amounts of labeled soleus proteins were mixed with unlabeled soleus and EDL muscle extracts, respectively. As a control we performed a crossover experiment by mixing labeled EDL with unlabeled EDL and Soleus extracts (Fig. 1). Forward and crossover experiments were performed with two biological replicates each. To decrease sample complexity, proteins were separated by one-dimensional SDS-PAGE. Each lane was separated into 15 slices following Coomassie-blue staining and subjected to in-gel digest with LysC to obtain lysine only containing peptides. We used the filter aided sample preparation (FASP) protocol in combination with isoelectric focusing of peptides (Offgel, Agilent) to increase our identification rate for proteins with reduced solubility. In total, we analyzed 123 fractions by LC-MS/MS using an LTQ-Orbitrap XL or an LTQ-Orbitrap XL Velos (Thermo-Fisher Scientific) mass spectrometer. Using a false discovery rate <1% we identified 537,282 MS/MS spectra, which resulted in the identification of 28,924 peptides corresponding to 3447 proteins with at least one unique peptide (supplemental Table S1). Quantifications (e.g. SILAC ratios) were obtained for 2163 proteins between Soleus and EDL muscle tissue. To facilitate data analysis and to provide public access to our skeletal muscle data set, an online proteomic database called QuantiMus was developed. This database contains all information obtained by using the MaxQuant software tool, including the number of identified peptides, unique peptides, Silac-ratios, PTMs and Mascot-scores (27Cox J. Mann M. MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification.Nat. Biotechnol. 2008; 26: 1367-1372Crossref PubMed Scopus (9154) Google Scholar). Both unregulated and regulated proteins are grouped into ratio bins. Data base searches create graphical overviews, which display localization of all identified peptides within a protein sequence (http://quantimus.mpi-bn.mpg.de). For the comparison of soleus and EDL muscles we selected only proteins that were quantified with at least one unique peptide in all Sol versus EDL and respective crossover measurements. In addition, by taking advantage of the QuantiMus database, we also included several proteins that were identified in soleus(h) versus EDL(l) or EDL(h) versus soleus(l) experiments but which lacked a corresponding ratio in control (soleus(h)/soleus(l) or EDL(h)/EDL(l)) experiments. This correction was necessary because a complete absence of proteins in either soleus or EDL generates misleading ratios. For example, slow troponin T was detected with a 25.5-fold change in Sol (h)/EDL (l) and with the inverse ratio of 0.02 in EDL (h)/Sol (l), which indicated low levels or even complete absence of slow troponins in fast muscle tissue. One challenge for mass spectrometry is the simultaneous detection of low and high abundant peptides within a complex protein sample such as extracts of total muscle. To test whether the peak intensity of detected SILAC-pairs would influence the overall ratio distribution and thus accuracy of our quantitative determinations we plotted the sum of the light and heavy peak intensities against the corresponding log2 ratios. As shown in (Fig. 2A), the ratio distribution of labeled versus non-labeled soleus sample was close to 1, indicating accurate quantification within the whole range of peak intensities. Of note, the overall ratio distribution is more scattered between soleus and EDL compared with the control soleus and soleus ratio (Fig. 2A) illustrating quantitative differences in the proteome composition between both muscle fiber types. A similar observation is made when binned log ratios of all quantified proteins are displayed in a frequency distribution plot: The distributions centered more closely on a ratio of 1 for the comparison between light and heavy muscle fibers of the same type, although they are clearly more dispersed when different fiber types were compared (Fig. 2C). The reproducibility of our quantitative measurements appeared very high as indicated by the Pearson correlation coefficient between both experiments (0.86) that was obtained by plotting all log ratios obtained from the soleus (h)/EDL (l) experiment against the respective crossover ratios (EDL (h)/soleus (l)) (Fig. 2B). Because extracts from the SILAC-labeled mouse served as an internal protein standard in our experiments we were able to calculate protein ratios between unlabeled soleus and unlabeled EDL simply by dividing ratio 2 through ratio 1 (see Fig. 1), which yields quantitative differences between both muscle types. Most detected proteins showed a ratio close to 1:1, which indicates equal amounts of proteins in both muscles. Myopodin, f.e., was detected with a Sol(h)/Sol(l) ratio of 1 and Sol(h)/EDL(l) ratio of 0.8, which finally resulted in a Sol(l)/EDL(l) ratio of 1.2. The crossover experiment showed for Myopodin also an EDL(l)/Sol(l) 1 ratio (Fig. 2D). Some proteins, such as the ATP citrate lyase were measured with a ratio of 4.3 between Sol(h)/Sol(l). However, a similar ratio of 4.9 was also determined comparing Sol(h)/EDL(l), resulting finally in a direct SOL(l)/EDL(l) ratio of 1.2. Similarly, Glutathione peroxidase 3 was detected with a Sol(h)/Sol(l) ratio of 0.4 and Sol(h)/EDL(l) ratio of 0.4, respectively, which again resulted in a SOL(l)/EDL(l) ratio of 1:1.1 (Fig. 2D). To estimate the total number of proteins that were differentially regulated between slow and fast muscle we calculated the geometric mean between the forward and crossover experiments and set the cutoff value to 1.5 or 2 (30Blagoev B. Ong S.E. Kratchmarova I. Mann M. Temporal analysis of phosphotyrosine-dependent signaling networks by quantitative proteomics.Nat. Biotechnol. 2004; 22: 1139-1145Crossref PubMed Scopus (586) Google Scholar, 31Mann M. Kelleher N.L. Precision proteomics: the case for high resolution and high mass accuracy.Proc. Natl. Acad. Sci. U.S.A. 2008; 105: 18132-18138Crossref PubMed Scopus (353) Google Scholar). Using this definition we identified 252 proteins that were enriched in the soleus and 299 proteins enriched in the EDL muscle (supplemental Table S1). To confirm the overall quality of our in vivo-SILAC approach we first focused on proteins that are well described components of either slow or fast muscle fibers, for