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Quantitative Proteomic Analysis Reveals Metabolic Alterations, Calcium Dysregulation, and Increased Expression of Extracellular Matrix Proteins in Laminin α2 Chain–deficient Muscle
2014; Elsevier BV; Volume: 13; Issue: 11 Linguagem: Inglês
10.1074/mcp.m113.032276
ISSN1535-9484
AutoresBruno Menezes de Oliveira, Cíntia Yuri Matsumura, Cibely Cristine Fontes de Oliveira, Kinga I. Gawlik, Helena Acosta, Patrik Wernhoff, Madeleine Durbeej,
Tópico(s)Knee injuries and reconstruction techniques
ResumoCongenital muscular dystrophy with laminin α2 chain deficiency (MDC1A) is one of the most severe forms of muscular disease and is characterized by severe muscle weakness and delayed motor milestones. The genetic basis of MDC1A is well known, yet the secondary mechanisms ultimately leading to muscle degeneration and subsequent connective tissue infiltration are not fully understood. In order to obtain new insights into the molecular mechanisms underlying MDC1A, we performed a comparative proteomic analysis of affected muscles (diaphragm and gastrocnemius) from laminin α2 chain–deficient dy3K/dy3K mice, using multidimensional protein identification technology combined with tandem mass tags. Out of the approximately 700 identified proteins, 113 and 101 proteins, respectively, were differentially expressed in the diseased gastrocnemius and diaphragm muscles compared with normal muscles. A large portion of these proteins are involved in different metabolic processes, bind calcium, or are expressed in the extracellular matrix. Our findings suggest that metabolic alterations and calcium dysregulation could be novel mechanisms that underlie MDC1A and might be targets that should be explored for therapy. Also, detailed knowledge of the composition of fibrotic tissue, rich in extracellular matrix proteins, in laminin α2 chain–deficient muscle might help in the design of future anti-fibrotic treatments. All MS data have been deposited in the ProteomeXchange with identifier PXD000978 (http://proteomecentral.proteomexchange.org/dataset/PXD000978). Congenital muscular dystrophy with laminin α2 chain deficiency (MDC1A) is one of the most severe forms of muscular disease and is characterized by severe muscle weakness and delayed motor milestones. The genetic basis of MDC1A is well known, yet the secondary mechanisms ultimately leading to muscle degeneration and subsequent connective tissue infiltration are not fully understood. In order to obtain new insights into the molecular mechanisms underlying MDC1A, we performed a comparative proteomic analysis of affected muscles (diaphragm and gastrocnemius) from laminin α2 chain–deficient dy3K/dy3K mice, using multidimensional protein identification technology combined with tandem mass tags. Out of the approximately 700 identified proteins, 113 and 101 proteins, respectively, were differentially expressed in the diseased gastrocnemius and diaphragm muscles compared with normal muscles. A large portion of these proteins are involved in different metabolic processes, bind calcium, or are expressed in the extracellular matrix. Our findings suggest that metabolic alterations and calcium dysregulation could be novel mechanisms that underlie MDC1A and might be targets that should be explored for therapy. Also, detailed knowledge of the composition of fibrotic tissue, rich in extracellular matrix proteins, in laminin α2 chain–deficient muscle might help in the design of future anti-fibrotic treatments. All MS data have been deposited in the ProteomeXchange with identifier PXD000978 (http://proteomecentral.proteomexchange.org/dataset/PXD000978). Congenital muscular dystrophy with laminin α2 chain deficiency, also known as MDC1A, 1The abbreviations used are:MDC1Amerosin congenital muscular dystrophy type 1ATMTtandem mass tags. 1The abbreviations used are:MDC1Amerosin congenital muscular dystrophy type 1ATMTtandem mass tags. is a severe muscle wasting disease for which there is no cure. MDC1A is caused by mutations in the LAMA2 gene that lead to complete or partial deficiency of laminin α2 chain (1Allamand V. 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Ervasti J.M. Leveille C.J. Slaughter C.A. Sernett S.W. Campbell K.P. Primary structure of dystrophin-associated glycoproteins linking dystrophin to the extracellular matrix.Nature. 1992; 355: 696-702Crossref PubMed Scopus (1195) Google Scholar, 5Talts J.F. Andac Z. Gohring W. Brancaccio A. Timpl R. Binding of the G domains of laminin α1 and α2 chains and perlecan to heparin, sulfatides, α-dystroglycan and several extracellular matrix proteins.EMBO J. 1999; 18: 863-870Crossref PubMed Scopus (398) Google Scholar, 6von der Mark H. Williams I. Wendler O. Sorokin L. von der Mark K. Pöschl E. Alternative splice variants of α7β1 integrin selectively recognize different laminin isoforms.J. Biol. Chem. 2002; 277: 6012-6016Abstract Full Text Full Text PDF PubMed Scopus (75) Google Scholar, 7Gawlik K.I. Durbeej M. Skeletal muscle laminin and MDC1A: pathogenesis and treatment strategies.Skelet. Muscle. 2011; 1: 9Crossref PubMed Scopus (86) Google Scholar). 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Ruegg M.A. An agrin minigene rescues dystrophic symptoms in a mouse model for congenital muscular dystrophy.Nature. 2001; 413: 302-307Crossref PubMed Scopus (198) Google Scholar). Thus, the structural link is broken, and the yet to be determined downstream intracellular signaling pathways are also interrupted. Consequently, laminin α2 chain–deficient muscle fibers undergo degeneration–regeneration cycles, but rather quickly regeneration fails and muscle fibers die by apoptosis/necrosis followed by a major replacement of muscle tissue with connective tissue (3Voit T. Tomé F.S. The congenital muscular dystrophies.in: Angel A. Franzini-Armstrong C. Myology. McGraw-Hill, New York2004: 1203-1238Google Scholar, 7Gawlik K.I. Durbeej M. Skeletal muscle laminin and MDC1A: pathogenesis and treatment strategies.Skelet. Muscle. 2011; 1: 9Crossref PubMed Scopus (86) Google Scholar). In order to unravel novel secondary molecular mechanisms, which could indicate new therapeutic targets, we decided to evaluate the protein expression profile in laminin α2 chain–deficient dy3K/dy3K muscle. Several proteomic profiling studies of dystrophin-deficient muscles (Duchenne muscular dystrophy) have been performed (13Doran P. Martin G. Dowling P. Jockusch H. Ohlendieck K. Proteome analysis of the dystrophin-deficient MDX diaphragm reveals a drastic increase in the heat shock protein cvHSP.Proteomics. 2006; 6: 4610-4621Crossref PubMed Scopus (113) Google Scholar, 14Gardan-Salmon D. Dixon J.M. Lonergan S.M. Selsby J.T. Proteomic assessment of the acute phase of dystrophin deficiency in mdx mice.Eur. J. Appl. Physiol. 2011; 111: 2763-2773Crossref PubMed Scopus (47) Google Scholar, 15Carberry S. Zweyer M. Swandulla D. Ohlendieck K. Proteomics reveals drastic increase of extracellular matrix proteins collagen and dermatopontin in the aged mdx diaphragm model of Duchenne muscular dystrophy.Int. J. Mol. Med. 2012; 30: 229-234Crossref PubMed Scopus (44) Google Scholar, 16Ge Y. Molloy M.P. Chamberlain J.S. Andrews P.C. Proteomic analysis of mdx skeletal muscle: great reduction of adenylate kinase 1 expression and enzymatic activity.Proteomics. 2003; 3: 1895-1903Crossref PubMed Scopus (77) Google Scholar, 17Guevel L. Lavoie J.R. Perez-Iratxeta C. Rouger K. Dubreil L. Feron M. Talon S. Brand M. Megeney L.A. Quantitative proteomic analysis of dystrophic dog muscle.J. Proteome Res. 2010; 10: 2449-2465Google Scholar, 18Matsumura C.Y. Menezes de Oliveira B. Durbeej M. Marques M.J. Isobaric tagging-based quantification for proteomic analysis: a comparative study of spared and affected muscles from mdx mice at the early phase of dystrophy.PLoS One. 2013; 8: e65831Crossref PubMed Scopus (35) Google Scholar, 19Rayavarapu S. Coley W. Cakir E. Jahnke V. Takeda S. Aoki Y. Grodish-Dressman H. Jaiswal J.K. Hoffman E.P. Brown K.J. Hathout Y. Nagaraju K. Identification of disease specific pathways using in vivo SILAC proteomics in dystrophin deficient mdx mouse.Mol. Cell. Proteomics. 2013; 12: 1061-1073Abstract Full Text Full Text PDF PubMed Scopus (80) Google Scholar, 20Lewis C. Carverry S. Ohlendieck K. Proteomic profiling of x-linked muscular dystrophy.J. Muscle Res. Cell. Motil. 2009; 30: 267-279Crossref PubMed Scopus (33) Google Scholar), as well as some with dysferlin-deficient muscles (Limb-girdle muscular dystrophy type 2B, Miyoshi myopathy) (21De la Torre C. Illa I. Faulkner G. Soria L. Robles-Cedeno R. Pereles-Dominguez-Perles R. De Luna N. Gallardo E. Proteomics identification of differentially expressed proteins in the muscle of dysferlin myopathy patients.Proteomics Clin. Appl. 2009; 3: 486-497Crossref PubMed Scopus (11) Google Scholar, 22de Morrée A. Hensbergen P.J. van Haagen H.H. Dragan I. Deelder A.M. 't Hoen P.A. Frants R.R. van der Maarel S.M. Proteomic analysis of the dysferlin protein complex unveils its importance for sarcolemmal maintenance and integrity.PLoS One. 2010; 5: e13854Crossref PubMed Scopus (56) Google Scholar). They all showed a great number of proteins that were differentially expressed in different dystrophic muscles and at different ages (13Doran P. Martin G. Dowling P. Jockusch H. Ohlendieck K. Proteome analysis of the dystrophin-deficient MDX diaphragm reveals a drastic increase in the heat shock protein cvHSP.Proteomics. 2006; 6: 4610-4621Crossref PubMed Scopus (113) Google Scholar, 14Gardan-Salmon D. Dixon J.M. Lonergan S.M. Selsby J.T. Proteomic assessment of the acute phase of dystrophin deficiency in mdx mice.Eur. J. Appl. Physiol. 2011; 111: 2763-2773Crossref PubMed Scopus (47) Google Scholar, 15Carberry S. Zweyer M. Swandulla D. Ohlendieck K. Proteomics reveals drastic increase of extracellular matrix proteins collagen and dermatopontin in the aged mdx diaphragm model of Duchenne muscular dystrophy.Int. J. Mol. Med. 2012; 30: 229-234Crossref PubMed Scopus (44) Google Scholar, 16Ge Y. Molloy M.P. Chamberlain J.S. Andrews P.C. Proteomic analysis of mdx skeletal muscle: great reduction of adenylate kinase 1 expression and enzymatic activity.Proteomics. 2003; 3: 1895-1903Crossref PubMed Scopus (77) Google Scholar, 17Guevel L. Lavoie J.R. Perez-Iratxeta C. Rouger K. Dubreil L. Feron M. Talon S. Brand M. Megeney L.A. Quantitative proteomic analysis of dystrophic dog muscle.J. Proteome Res. 2010; 10: 2449-2465Google Scholar, 18Matsumura C.Y. Menezes de Oliveira B. Durbeej M. Marques M.J. Isobaric tagging-based quantification for proteomic analysis: a comparative study of spared and affected muscles from mdx mice at the early phase of dystrophy.PLoS One. 2013; 8: e65831Crossref PubMed Scopus (35) Google Scholar, 19Rayavarapu S. Coley W. Cakir E. Jahnke V. Takeda S. Aoki Y. Grodish-Dressman H. Jaiswal J.K. Hoffman E.P. Brown K.J. Hathout Y. Nagaraju K. Identification of disease specific pathways using in vivo SILAC proteomics in dystrophin deficient mdx mouse.Mol. Cell. Proteomics. 2013; 12: 1061-1073Abstract Full Text Full Text PDF PubMed Scopus (80) Google Scholar, 20Lewis C. Carverry S. Ohlendieck K. Proteomic profiling of x-linked muscular dystrophy.J. Muscle Res. Cell. Motil. 2009; 30: 267-279Crossref PubMed Scopus (33) Google Scholar, 21De la Torre C. Illa I. Faulkner G. Soria L. Robles-Cedeno R. Pereles-Dominguez-Perles R. De Luna N. Gallardo E. Proteomics identification of differentially expressed proteins in the muscle of dysferlin myopathy patients.Proteomics Clin. Appl. 2009; 3: 486-497Crossref PubMed Scopus (11) Google Scholar, 22de Morrée A. Hensbergen P.J. van Haagen H.H. Dragan I. Deelder A.M. 't Hoen P.A. Frants R.R. van der Maarel S.M. Proteomic analysis of the dysferlin protein complex unveils its importance for sarcolemmal maintenance and integrity.PLoS One. 2010; 5: e13854Crossref PubMed Scopus (56) Google Scholar). However, proteomic analyses of laminin α2 chain–deficient muscle have not yet been performed. We here used multidimensional protein identification technology with tandem mass tags (TMT), a powerful shotgun label-based proteomic method that separates peptides in two-dimensional liquid chromatography (23Washburn M. Wolters D. Yates III, J. Large scale analysis of the yeast proteome by multidimensional protein identification technology.Nat. Biotechnol. 2001; 19: 242-247Crossref PubMed Scopus (4077) Google Scholar, 24Thompson A. Schäfer J. Kuhn K. Kienle S. Schwarz J. Schmidt G. Neumann T. Johnstone R. Mohammed A.K. Hamon C. Tandem mass tags: a novel quantification strategy for comparative analysis of complex protein mixtures by MS/MS.Anal. Chem. 2003; 75: 1895-1904Crossref PubMed Scopus (1709) Google Scholar). We identified around 100 proteins that were differentially expressed in laminin α2 chain–deficient gastrocnemius and diaphragm muscles relative to the corresponding wild-type muscles, and the differential expression of selected proteins was verified with Western blot analysis or immunofluorescence. merosin congenital muscular dystrophy type 1A tandem mass tags. merosin congenital muscular dystrophy type 1A tandem mass tags. Four-week-old laminin α2 chain–deficient dy3K/dy3K mice and wild-type littermates (n = 15 each) were used (25Carmignac V. Quere R. Durbeej M. Proteasome inhibition improves the muscle of laminin α2 chain-deficient mice.Hum. Mol. Genet. 2011; 20: 541-552Crossref PubMed Scopus (55) Google Scholar). Mice were maintained in the animal facilities of BMC (Lund) according to the animal care guidelines. All mouse experimentation was approved by the Malmö/Lund (Sweden) ethical committee for animal research (permit number M62-09). Animals were sacrificed by means of cervical dislocation, and the diaphragm and gastrocnemius muscles were collected, frozen in liquid nitrogen, and pulverized using a mortar and pestle. Subsequently, we used the same experimental setup as previously described (18Matsumura C.Y. Menezes de Oliveira B. Durbeej M. Marques M.J. Isobaric tagging-based quantification for proteomic analysis: a comparative study of spared and affected muscles from mdx mice at the early phase of dystrophy.PLoS One. 2013; 8: e65831Crossref PubMed Scopus (35) Google Scholar). Three different pools for each group (dy3K/dy3K and wild-type mice) were made, each composed of muscles from five animals (Table I). Protein was extracted in lysis buffer (10 mm NaHCO3, 5% SDS containing freshly added protease and phosphatase inhibitors (Roche)) via ultrasonication (3 × 5 s at 4 °C). Samples were centrifuged for 5 min (15,000 × g), and supernatant was collected. The protein concentration was determined using a BCA Protein Assay Kit (Pierce). Subsequently, samples were processed according to the instructions for the TMT isobaric mass tagging kits and reagents (Pierce). Briefly, 100 μg of proteins per condition were placed in an Eppendorf tube. Forty-five microliters of 100 mm triethyl ammonium bicarbonate were added to the sample, and the sample was adjusted to a final volume of 100 μl with ultrapure water. Five microliters of 200 mm TCEP (reducing agent) were added and incubated at 55 °C for 1 h. Five microliters of 375 mm iodoacetamide was added, and samples were incubated for 30 min while protected from light. After this, samples were precipitated overnight with six volumes of prechilled (−20 °C) acetone. Finally, protein pellets were resuspended in 100 μl of 100 mm triethyl ammonium bicarbonate. At this point trypsin was added, and proteolytic digestion was allowed overnight at 37 °C. Label reagents were prepared at room temperature, and 41 μl of each reagent were used to label 100 μg of protein. Besides our four samples to be analyzed, a standard one, composed of equal fractions of the four others, was labeled. This standard sample was our reference, used to define the relative protein amounts for each analyzed sample. Our labeling design allowed a label swap, in order to avoid possible bias due to technical errors (Table I). The label reaction proceeded for 1 h at room temperature, and subsequently reactions were quenched with 8 μl of 5% hydroxylamine for 15 min, mixed, and stored at −80 °C. Pooled TMT-labeled samples were fractionated by strong cation exchange (Applied Biosystems, Foster City, CA) using 500 μl of elution buffer (KH2PO4, 25 mm acetonitrile, pH 2.9) containing increasing concentrations of KCl (30, 60, 90, 120, 240, 300, 420, and 500 mm KCl) and collected as fractions 1–8, respectively. The fractions were cleaned on Ultra Microspin C18 columns (The Nest Group, Southborough, MA), dried, and resuspended in 30 μl of 0.1% formic acid.Table IExperimental setup for gastrocnemius (GAST) and diaphragm (DIA) muscles under two conditions (dy3K/dy3K and wild-type (WT)) and with three biological replicates (pools 1, 2, and 3). The internal standard was a mixture of all samples Open table in a new tab Samples were analyzed at the Gothenburg Proteomics Core, Gothenburg University, following the protocol described below. Desalted and dried fractions were reconstituted into 0.1% formic acid and analyzed on an LTQ-OrbitrapXL (Thermo Fisher Scientific) interfaced with an in-house-constructed nano-LC column. Two-microliter sample injections were made with an HTC-PAL autosampler (CTC Analytics AG, Zwingen, Switzerland) connected to an Agilent 1200 binary pump (Agilent Technologies, Santa Clara, CA). The peptides were trapped on a pre-column (40 × 0.075 mm inner diameter) and separated on a reversed phase column (200 × 0.050 mm). Both columns were packed in-house with 3-μm Reprosil-Pur C18-AQ particles. The flow through the analytical column was reduced by a split to ∼100 nl/min, and the gradient was as follows; 0–6 min, 0.1% formic acid; 6–76 min, 7% to 35% acetonitrile, 0.1% formic acid; 76–79 min, 40% to 80% acetonitrile, 0.1% formic acid. LTQ-OrbitrapXL settings were as follows: spray voltage, 1.4 kV; 1 microscan for MS1 scans at 60,000 resolution (m/z 400); full MS mass range, m/z 400–2000. The LTQ-OrbitrapXL was operated in a data-dependent mode with one MS1 Fourier transform MS scan of precursor ions followed by collision-induced dissociation and high-energy collision dissociation MS2 scans of the three most abundant doubly, triply, and quadruply protonated ions in each Fourier transform MS scan. The settings for the MS2 were as follows: 1 microscan for high-energy collision dissociation MS2 at 7500 resolution (at m/z 400), mass range of m/z 100–2000 with a collision energy of 50%, and 1 microscan for collision-induced dissociation MS2 with a collision energy of 30%. Dynamic exclusion of a precursor selected for MS2 was used for 120 s after one repeat, allowing most of the co-eluting precursors to be selected for MS2. All samples were analyzed a second time as described above, and also a third time using an exclusion list of all m/z within a 3-min retention window, already passing the identification criteria within the TMT set (eight fractions) in the database search. MS raw data files from all eight strong cation exchange fractions per one TMT set and three MS runs were merged for relative quantification and identification using Proteome Discoverer version 1.3 (Thermo Fisher Scientific). The database search was performed with the Mascot search engine using the following criteria: Mus musculus in Swiss-Prot protein database from April 2012 (535,698 entries); MS peptide tolerance of 10 ppm; MS/MS tolerance of 0.5 Da; trypsin digestion allowing one missed cleavage with variable modifications (methionine oxidation, cysteine methylthiol) and fixed modifications (N-terminal TMT6-plex label, lysine TMT6-plex label). The detected protein threshold in the software was set to a confidence using the 1% false discovery rate method, and identified proteins were grouped by shared sequence to minimize redundancy. For quantification, the ratios of TMT reporter ion intensities in MS/MS spectra (m/z 126.12, 127.13, 128.13, 129.14, 130.14) from raw datasets were used to calculate fold changes between samples via the relative ratio to the reference pool. Only peptides unique for a given protein were considered for relative quantitation, excluding those common to other isoforms or proteins of the same family. Only peptides with a score of >10 and below the Mascot significance threshold filter of p = 0.05 were included. Single peptide identifications required a score equal to or above the Mascot identity threshold. Normalization of protein median was used, and the median of peptides was used for determining protein ratio; the resulting ratios were exported into Excel for manual data interpretation. The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium (http://proteomecentral.proteomexchange.org) via the PRIDE partner repository (26Vizcaíno J.A. Deutsch E.W. Wang R. Csordas A. Reisinger F. Ríos D. Dianes J.A. Sun Z. Farrah T. Bandeira N. Binz P.A. Xenarios I. Eisenacher M. Mayer G. Gatto L. Campos A. Chalkley R.J. Kraus H.J. Albar J.P. Martinez-Bartolomé S. Apweiler R. Omenn G.S. Martens L. Jones A.R. Hermjakob H. ProteomeXchange provides globally co-ordinated proteomics data submission and dissemination.Nat. Biotechnol. 2014; 32: 223-226Crossref PubMed Scopus (2071) Google Scholar) with the dataset identifier PXD000978. Please see the supplemental material for annotated spectra and Table I for the experimental setup. To determine which proteins were differentially expressed, only proteins quantified in all three runs (see Table I for the experimental setup) were used; average values were compared, and Student's t test was performed to validate differences (p < 0.05) (supplemental Tables S1–S3). Still, compared proteins should present a variation coefficient of less than 25% and a protein ratio less than 1.25 or greater than 1.25. In order to avoid multiple hypothesis test error, q-values were estimated (27Karp N.A. McCormick P.S. Russell M.R. Lilley K.S. Experimental and statistical considerations to avoid false conclusions in proteomics studies using differential in-gel electrophoresis.Mol. Cell. Proteomics. 2007; 6: 1354-1364Abstract Full Text Full Text PDF PubMed Scopus (150) Google Scholar, 28Storey J.D. Tibshirani R. Statistical significance for genome wide studies.Proc. Natl. Acad. Sci. U.S.A. 2003; 100: 9440-9445Crossref PubMed Scopus (7083) Google Scholar). Also, to ensure the reproducibility of our findings, the retrospective statistical power was calculated using the free statistical software package R, according to Ref. 29Levin Y. The role of statistical power analysis in quantitative proteomics.Proteomics. 2011; 11: 2565-2567Crossref PubMed Scopus (74) Google Scholar. Only results for β ≥ 0.8 were validated as true. Muscles were collected and proteins were extracted as described above, but from a different animal cohort (n = 4 for each genotype). Thirty micrograms of total protein were applied per well. SDS-PAGE was performed according to Laemmli's protocol using 12% pre-casted gels in a Mini Protean Tetracell Electrophoresis System (Bio-Rad), followed by electrophoretic transfer. PVDF membranes were blocked in 5% non-fat dry milk in TBS with 0.05% Tween-20 overnight at 4 °C. Membranes were incubated with primary antibodies (rabbit anti-isocitrate dehydrogenase, 1:1000, NBP1–31599, Novus (Littleton, CO); mouse anti-SERCA1 ATPase, 1:4000, ab2819, Abcam; mouse anti-calsequestrin 1 and 2, 1:1000, ab3516, Abcam (Cambridge, UK); goat anti-annexin A1, 1:1000, NBP1–18842) for 1 h at room temperature and subsequently washed three times for 10 min in TBS with 5% Tween-20. Membranes were once again blocked in 5% non-fat dry milk for 1 h and then incubated with secondary antibodies (1 h at room temperature). After three 10-min washes with TBS with 5% Tween-20, membranes were incubated in ECL chemiluminiscence solution (Amersham Biosciences), exposed to Hyperfilm (Amersham Biosciences), and developed (Curix 60, AGFA, Mortsel, Belgium). We applied the Mann–Whitney U test to assess whether the difference in protein expression was statistically significant. Statistical significance was accepted for p < 0.05. Skeletal muscle (quadriceps and diaphragm) sections of 7 μm were processed for immunofluorescence analyses following standard procedures (30Gawlik K. Miyagoe-Suzuki Y. Ekblom P. Takeda S. Durbeej M. Laminin α1 chain reduces muscular dystrophy in laminin α2 chain deficient mice.Hum. Mol. Genet. 2004; 13: 1775-1784Crossref PubMed Scopus (109) Google Scholar) with rabbit polyclonal antibodies directed against periostin (1:200, NBP1–30042, Novus) and galectin-1 (1:100, NBP1–89791, Novus). Sections were analyzed using a Zeiss Axioplan fluorescence microscope. Images were captured using an ORCA 1394 ER digital camera with the Openlab 3 software. Two-dimensional polyacrylamide gel electrophoresis has mainly been used as the proteomic approach to analyze dystrophic muscle (13Doran P. Martin G. Dowling P. Jockusch H. Ohlendieck K. Proteome analysis of the dystrophin-deficient MDX diaphragm reveals a drastic increase in the heat shock protein cvHSP.Proteomics. 2006; 6: 4610-4621Crossref PubMed Scopus (113) Google Scholar, 14Gardan-Salmon D. Dixon J.M. Lonergan S.M. Selsby J.T. Proteomic assessment of the acute phase of dystrophin deficiency in mdx mice.Eur. J. Appl. Physiol. 2011; 111: 2763-2773Crossref PubMed Scopus (47) Google Scholar, 15Carberry S. Zweyer M. Swandulla D. Ohlendieck K. Proteomics reveals drastic increase of extracellular matrix proteins collagen and dermatopontin in the aged mdx diaphragm model of Duchenne muscular dystrophy.Int. J. Mol. Med. 2012; 30: 229-234Crossref PubMed Scopus (44) Google Scholar, 16Ge Y. Molloy M.P. Chamberlain J.S. Andrews P.C. Proteomic analysis of mdx skeletal muscle: great reduction of adenylate kinase 1 expression and enzymatic activity.Proteomics. 2003; 3: 1895-1903Crossref PubMed Scopus (77) Google Scholar, 17Guevel L. Lavoie J.R. Perez-Iratxeta C. Rouger K. Dubreil L. Feron M. Talon S. Brand M. Megeney L.A. Quantitative proteomic analysis of dystrophic dog muscle.J. Proteome Res. 2010; 10: 2449-2465Google Scholar). To overcome several drawbacks associated with this technique, we decided to use a label-based proteomic approach combining TMT labels and the multidimensional protein identification technology method. In this kind of analysis, whole samples are digested with a protease prior to identification and quantification of proteins in the sample. In our case we used trypsin, and peptides were labeled with an isobaric tag (TMT) for quantification (24Thompson A. Schäfer J. Kuhn K. Kienle S. Schwarz J. Schmidt G. Neumann T. Johnstone R. Mohammed A.K. Hamon C. Tandem mass tags: a novel quantification strategy for comparative analysis of complex protein mixtures by MS/MS.Anal. Chem. 2003; 75: 1895-1904Crossref PubMed Scopus (1709) Google Scholar). Subsequently, samples were fractionated using biphasic capillary columns packed with strong cation exchange and reversed phase material to separate digested and TMT-labeled whole protein mixtures. Labeled peptides were thus separated based on their charge and hydrophobicity, and the identification and quantification of peptides in eluted fractions was achieved using a mass spectrometer (peptides were identified through database searching and quantified by evaluation of reporter ion intensities from labels) (31Kline K.G. Wu C.C. MudPIT analysis: application to human heart tissue.Methods Mol. Biol. 2009; 528: 281-293Crossref PubMed Scopus (21) Google Scholar, 32Martins de Souza D. Oliveir
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