Mutation-independent Proteomic Signatures of Pathological Progression in Murine Models of Duchenne Muscular Dystrophy.

The absence of the dystrophin protein in Duchenne muscular dystrophy (DMD) results in myofiber fragility and a plethora of downstream secondary pathologies. Although a variety of experimental therapies are in development, achieving effective treatments for DMD remains exceptionally challenging, not least because the pathological consequences of dystrophin loss are incompletely understood. Here we have performed proteome profiling in tibialis anterior muscles from two murine DMD models (mdx and mdx52) at three ages (8, 16, and 80 weeks of age), all n = 3. High-resolution isoelectric focusing liquid chromatography-tandem MS (HiRIEF-LC-MS/MS) was used to quantify the expression of 4974 proteins across all 27 samples. The two dystrophic models were found to be highly similar, whereas multiple proteins were differentially expressed relative to WT (C57BL/6) controls at each age. Furthermore, 1795 proteins were differentially expressed when samples were pooled across ages and dystrophic strains. These included numerous proteins associated with the extracellular matrix and muscle function that have not been reported previously. Pathway analysis revealed multiple perturbed pathways and predicted upstream regulators, which together are indicative of cross-talk between inflammatory, metabolic, and muscle growth pathways (e.g. TNF, INFγ, NF-κB, SIRT1, AMPK, PGC-1α, PPARs, ILK, and AKT/PI3K). Upregulation of CAV3, MVP and PAK1 protein expression was validated in dystrophic muscle by Western blot. Furthermore, MVP was upregulated during, but not required for, the differentiation of C2C12 myoblasts suggesting that this protein may affect muscle regeneration. This study provides novel insights into mutation-independent proteomic signatures characteristic of the dystrophic phenotype and its progression with aging.


Introduction
Duchenne muscular dystrophy (DMD) is a severe, X-linked, pediatric neuromuscular disorder characterized by progressive muscle wasting, loss of ambulation around age 10, and cardiorespiratory failure that is ultimately fatal (1)(2)(3)(4). The disease is caused by mutations in the DMD gene which disrupt the translation reading frame, leading to loss of dystrophin protein expression (5,6). Dystrophin is important for mechanical force transduction, and is also involved in signaling functions (7)(8)(9)(10)(11), in part due to its role as an organizing center for the dystrophin-associated protein complex (DAPC). Absence of dystrophin leads to myofiber fragility, sensitivity to contractile damage, chronic cycles of myonecrosis and regeneration, persistent inflammation, and progressive fibro/fatty muscle degeneration (12)(13)(14).
The DMD gene consists of 79 exons and contains at least seven internal promoters (15) which give rise to the various dystrophin isoforms (e.g. Dp427, Dp260, Dp140, Dp116, Dp71, and Dp40) ( Figure S1). Some isoforms are ubiquitously expressed (e.g. Dp71) (16), while others are expressed in a more tissue-restricted pattern (such as Dp260, which is the retinal isoform of dystrophin) (17). As a result, the genomic locations of DMD-causing mutations may differentially affect the expression of these various isoforms, and by extension disease manifestation. For example, loss of the Dp71 isoform has been associated with cognitive impairment (18,19).
Several dystrophic mouse strains have been developed to investigate DMD pathophysiology, and test novel therapeutics in vivo (20). The most commonly used model is the mdx mouse, which carries a nonsense mutation in exon 23 leading to loss of the major muscle dystrophin isoform Dp427 ( Figure S1) (21,22). While the mdx mouse recapitulates some aspects of DMD pathology (23), it is generally considered to exhibit mild muscular dystrophy with only a small reduction in life-span (24). mdx mice undergo a brief period of degeneration and regeneration between 2 and 12 weeks of age (25)(26)(27), with more pronounced muscle pathology and cardiomyopathy manifesting much later in life (28,29). Importantly, the exon 23 mutation observed in the mdx mouse does not typically occur in boys with DMD (30), which has motivated the development of more patient-relevant dystrophic mouse models. To this end, Araki et al. generated the mdx52 mouse model in which Dmd exon 52 is deleted, leading to the absence of the Dp260 and Dp140 isoforms in addition to Dp427 ( Figure S1) (31). Deletions in the so-called 'hot-spot' region (DMD exons [45][46][47][48][49][50][51][52][53][54][55] are some of the most commonly observed mutations in boys with DMD, thereby making this model more patient-relevant than the more widely used mdx mouse (6). We recently reported differences in the number of dystrophinpositive revertant fibers and regenerating fibers between mdx and mdx52 mice (32).
Specifically, mdx mice contained higher numbers of revertant fibers at all ages tested (2-18 months of age), whereas mdx52 mice contained elevated numbers of centrally-nucleated fibers at 2 months of age only (32). Importantly, the mdx52 model also allows for the testing of patient mutation-relevant exon skipping strategies in vivo (e.g. targeting exon 51 or exon 53) (33)(34)(35).
Despite significant research effort, many aspects of dystrophic pathology remain unclear. As such, there is a need to understand the complex molecular mechanisms underlying DMD at the level of gene and protein expression. Transcriptomics methodologies have enabled the simultaneous measurement of tens of thousands of genes in the muscles of both dystrophindeficient animal models and DMD patient biopsy samples (36)(37)(38)(39)(40). We have previously compared global transcript and protein expression in mdx versus control muscle (39).
While the global quantification of nucleic acid transcripts is relatively simple using digital gene expression analysis (i.e. RNA-sequencing) or hybridization methods (e.g. DNA microarrays), proteomics analysis is substantially more challenging. Early proteomics studies in dystrophic muscle utilized 2D-electrophoresis to identify differentially abundant proteins, but results were limited to only a handful of differential expression calls (44,45). The use of other methodologies such as iCAT, in vivo SILAC, and label-free approaches resulted in the identification of further differentially expressed proteins in dystrophic muscle, although these studies were still limited by their low proteomic coverage (~1,000 proteins quantified, or less) (46)(47)(48). Global proteome profiling in fibrous tissues such as skeletal muscle is complicated by the presence of very high concentrations of a few structural proteins, such as actins and myosins (49). Peptides derived from these proteins mask signals from lowly abundant proteins and thereby limit the depth of proteome coverage that can be achieved. To increase analytical depth, we recently applied high-resolution sample pre-fractionation based on narrow-range isoelectric focusing of peptides to quantify expression of over 3,272 proteins in mouse muscle (39).
To date, there have been relatively few high-resolution proteomics studies in dystrophic muscle, and fewer still that have measured global changes in protein expression in muscle throughout the progression of pathology over time (39,47,50). Here, we have performed mass spectrometry-based proteomic profiling in both the mdx and mdx52 DMD mouse models compared with wild-type controls (WT) at three ages representing the different stages of dystrophic pathology. We show that this state-of-the-art proteomic strategy has uncovered previously unidentified pathological pathways in dystrophic mouse models, which are potential therapeutic targets for DMD.

Animals
Mice were housed under 12:12h light-dark conditions with food and water ad libitum. All experimental protocols in this study were approved by the Experimental Animal Care and Use Committee of the National Institute of Neuroscience, NCNP, Japan. mdx52 mice were generated at our facility at the NCNP (31) and have been back-crossed with C57BL/6 mice for more than 10 generations. mdx mice on a C57BL/6 background were kindly provided by Dr T. Sasaoka (Brain Research Institute, Niigata University, Niigata, Japan). C57BL/6 mice were used as controls to match the background of the dystrophic strains (i.e. mdx52 and mdx). Serum and tissues from each strain were collected at 4, 8, 16, 24, 48, and 80 weeks of age (n=3-5 per group). Tibialis anterior (TA) muscle of all strains at 8, 16, and 80 weeks (n=3) was subsequently cryosectioned, collecting 50 sections of 10 μm for each sample.

Cell culture
C2C12 myoblasts were cultured at 37°C with 5% CO2 in Dulbecco's modified eagle medium (DMEM) containing 20% fetal bovine serum (FBS) and 1% antibiotics/antimycotics (growth medium: GM) (all Life Technologies, Carlsbad, CA, USA). DMEM supplemented with 2% horse serum (HS) and 1% antibiotics/antimycotics (differentiation medium: DM) was utilized to differentiate C2C12 myoblasts for 3-6 days to form multinucleated myotubes. C2C12 myoblasts were seeded in 24-well and 6-well plates at 100,000 cells and 400,000 cells per well respectively. For transfections, cells were incubated in GM before addition of 50 nM siRNA complexes (either targeting Mvp or a control siRNA, ON-TARGETplus siRNA: Dharmacon, Cambridge, UK). Complex formation was performed in the absence of serum, and cells collected after three days in DM.

HiRIEF-nanoLC-MS/MS-based proteomics
Sample preparation for mass spectrometry Peptides were labelled using the TMT10plex Isobaric Label Reagent Set according to the manufacturer's protocol (Thermo Fisher Scientific) and cleaned-up using strata-X-C-cartridges (Phenomenex, Torrance, CA, USA).

IPG-IEF of peptides.
TMT labelled peptides were separated by immobilized pH gradient -isoelectric focusing (IPG-IEF) on pH 3.7-4.9 strips (250 μg peptides per strip) as described previously (39,53). Peptides were extracted from the strips using a prototype liquid handling robot, supplied by GE Healthcare Bio-Sciences AB. A plastic device with 72 wells was put onto each strip and 50 μl of Milli-Q water was added to each well. After incubation for 30 minutes, the liquid was transferred to a 96 well plate and the extraction was repeated 2 more times. The extracted peptides were dried in speed vac for storage and dissolved in 3% acetonitrile, 0.1% formic acid before MS analysis.

Peptide and protein identification
All Orbitrap data were searched using SequestHT under the software platform Proteome Discoverer 1.4 (Thermo Fisher Scientific) against the Ensembl 78 mouse protein database (53,838 protein entries) and filtered to a 1% false discovery rate (FDR). A precursor mass tolerance of 10 ppm, and product mass tolerances of 0.02 Da for HCD-FTMS were used.
Further settings used were: trypsin with 2 missed cleavage; iodoacetamide on cysteine and TMT on lysine and N-terminal as fixed modifications; and oxidation of methionine as variable modification. Quantification of TMT-10plex reporter ions was performed using Proteome Discoverer on HCD-FTMS tandem mass spectra using an integration window tolerance of 10 ppm. Only peptides unique to a protein group were used for quantitation. A pool of all samples was used in one TMT tag as a linker (denominator) between TMT sets. For data analysis, proteins that could not be identified in all samples were filtered out from the final list and values were log2 transformed.

Western blot
Tissue samples were lysed in 75 mM Tris-HCL (pH 6.8), supplemented with 10% SDS, 5% 2mercapto-ethanol and 3% protease inhibitors (Sigma-Aldrich, Dorset, UK). These were then heated at 100°C for 3 minutes and centrifuged at 13,000 g for 10 minutes. Cells were lysed in RIPA lysis buffer (ThermoFisher Scientific) supplemented with protease and phosphatase inhibitors (Sigma-Aldrich), incubated at 4°C for 5 minutes, then centrifuged at 10,000 g for 5 minutes. Protein was stored at -80°C. Protein lysates were resolved on polyacrylamide gels and  Table S1. Equal protein loading was determined by Coomassie Brilliant Blue (CBB) staining for tissue samples and GAPDH immunoblot for cell culture samples was utilized to determine protein loading.

RT-qPCR
RNA was extracted using TRIzol reagent according to manufacturer's instructions. Briefly, samples were homogenized in TRIzol (ThermoFisher Scientific) after which chloroform was added. Samples were centrifuged resulting in phase separation whereby the supernatant was removed and washed with 500 μl of isopropanol, followed by a 10-minute incubation at room temperature and centrifugation. Pellets were subsequently washed with 75% ethanol, air-dried and resuspended in nuclease-free water. RNA was stored at -80°C.
High-capacity cDNA Reverse Transcription Kit (ThermoFisher Scientific) was used for cDNA synthesis according to manufacturer's instructions. Power SYBR Green master mix was used for gene expression analysis according to manufacturer's instructions, using 1:10 diluted cDNA (primer sequences can be found in Table S2). Values were normalized to the geometric average of two stable reference genes, Rplp0 and Tbp for tissue samples and Rplp0 for cells.
RT-qPCR data was analyzed using the Pfaffl method and PCR efficiencies determined by LinRegPCR (Amsterdam Medical Center, Amsterdam, the Netherlands).

Immunofluorescence
Cells were fixed in 4% paraformaldehyde for 10 minutes and permeabilized for 15 minutes in

Statistics and bioinformatics
Statistical analysis was performed using R i386 3.2.0 (The R Project) and R Studio (Boston, MA, USA). TMT ratios were log2 transformed and significance was tested by t-test for twosample comparisons and one-way ANOVA for comparisons between more than two groups

Experimental design
To investigate differential protein expression in dystrophic muscle we collected tibialis anterior (TA) muscles from two dystrophic mouse models (mdx and mdx52). Muscles were collected from age-and sex-matched C57BL/6 WT controls (all mice in this study were male). The choice of ages was based on our previous observations of differences in muscle regeneration and the number of revertant fibers between the mdx and mdx52 strains (32), and on the analysis of serum miRNA levels ( Figure S2). Our group and others have shown that the muscleenriched microRNAs (the myomiRs: miR-1a-3p, miR-133a-3p, and miR-206-3p) are highly elevated in serum of dystrophic animal models and DMD patients, indicative of increased muscle turnover (56)(57)(58)(59)(60)(61)(62). We therefore analyzed serum myomiR expression at different ages throughout the course of dystrophic pathology in order to characterize myomiR abundance patterns in the two dystrophic mouse models with age. Serum from animals at 4, 8, 16, 24, 48, and 80 week-old mice was collected, RNA extracted, and myomiRs measured by small RNA TaqMan RT-qPCR. miR-223-3p was included as a non-myomiR endogenous control that was expected to be relatively stable based on previous studies (52,56,63). MyomiR expression was elevated in mdx and mdx52 relative to WT at all ages ( Figure S2). Interestingly, myomiR levels in mdx52 serum peaked earlier than for the mdx mice (i.e. 8 weeks versus 16 weeks), consistent with the earlier peak of muscle regeneration observed in this model (32). At the later ages, myomiR abundance remained significantly increased relative to controls. Based on these findings, we selected the 8, 16, and 80-week ages in which to perform proteomic profiling across the mouse strains. The 80-week samples were included as a representative of 'aged' muscle in which dystrophic pathology is more advanced (12,58).
Using these samples, we aimed to identify differences between the mdx and mdx52 models which may account for their distinct phenotypes, establish a mutation-independent proteomic signature of the dystrophic condition, and to investigate alterations in the dystrophic proteome associated with pathological progression.

Proteomics analysis in dystrophic muscle
TA protein lysates from male C57BL/6, mdx, and mdx52 mice at 8, 16, and 80 weeks of age (n=3) were analyzed by HiRIEF-LC-MS/MS (53) and samples labelled using TMT chemistry, allowing for multiplexing of up to 10 samples during a single LC-MS/MS run. Three separate runs were performed whereby a common bridge sample (generated by mixing protein lysates from each sample in equal amounts) was included in each set of 10 samples. TMT ratios were calculated relative to the bridge sample, thereby allowing for direct comparison of samples analyzed on different runs, and effectively increasing the level of multiplexing to 27-plex.
A total of 7,111 proteins were identified, of which 4,974 (70%) were quantified in all samples and used for proteomics analysis (Figure 1). Details of peptides analyzed for protein identification and quantification are shown in Figure S3. Notably, methodological performance was highly similar when comparing between the three separate LC-MS/MS runs ( Figure S3B). This performance is a substantial improvement on our previous data using similar methodology (iTRAQ labelling with HiRIEF-LC-MS/MS, where 3,057 proteins were quantifiable (39)). Normalized TMT ratios for the full dataset are provided in File S1.
Analysis of the protein cumulative distribution frequency showed that only 25 proteins accounted for 50% of the detected protein mass, which included myosin heavy chain proteins To assess the performance of the proteomics analysis we examined the expression of proteins which are known to be differentially expressed in dystrophic muscle. Dystrophin (DMD) was observed to be highly down-regulated in the dystrophic animals at all ages, consistent with its genetic disruption. Accordingly, expression of the DAPC components: NOS1, SGCA, SGCB, SGCG, DAG1, SNTA1, and DTNA were all significantly down-regulated in both mdx and mdx52 mice at all ages ( Figure S5). Loss of dystrophin leads to the mislocalization and subsequent destabilization of DAPC components (64). As a result, down-regulation of DAPC proteins is an established feature of dystrophic muscle (39,65). In contrast, UTRN (utrophin) was significantly up-regulated in dystrophic muscle ( Figure S5). Utrophin is a paralog of dystrophin which is primarily expressed at the neuromuscular and myotendinous junctions, and is known to be up-regulated at the dystrophin-deficient sarcolemma (39,(66)(67)(68)(69). Similarly, aquaporin-4 (AQP4), periostin (POSTN), myostatin (MSTN), and biglycan (BGN) were also differentially expressed in dystrophic muscle as reported previously (39,(70)(71)(72)(73). In summary, consistent differential expression of these proteins in both dystrophic strains across all ages ( Figure S5) demonstrates the robustness of both our proteomics analysis and multiplexing strategy.

A mutation-independent dystrophic proteome signature
No significant changes in protein expression were detected when comparing the mdx and mdx52 dystrophic strains by t-test (Figure S6A), consistent with the close clustering of these samples in the heatmap and PCA analyses described above (Figure 2). Absolute fold changes for the mdx52 vs mdx comparisons were lower than for other comparisons (Figure S7A), and the coefficients of variation for the mdx52 samples are similar to, or lower than, other experimental groups (indicating that these samples are not inherently noisy, Figure S7B).
Together these data suggest that the lack of significant differential expression calls in the mdx52 vs mdx comparisons are a consequence of the high similarity of the muscle proteomes in these strains, rather than a deficiency in statistical power.
Separate t-tests were performed in order to compare mdx vs WT ( Figure S6B) or mdx52 vs WT at each age ( Figure S6C). Relatively few differentially expressed proteins (<35) were observed at the 8 week and 16 week ages. In contrast, several hundred differentially expressed proteins were identified in the 80-week muscle samples, of which 199 were common between the mdx and mdx52 strains ( Figure S6D).
Considering the similarity in proteomes between the two dystrophic strains, and the relatively small number of differences detected when comparing each dystrophic model against the WT controls, we decided to pool the TMT ratios for the mdx and mdx52 samples at each age in order to increase group size (to n=6) and thereby boost statistical power. Differentially expressed proteins in the pooled dystrophic muscles were assessed by t-test at each age and 805, 837, and 1,618 differentially expressed (adjusted P<0.01) proteins identified at 8, 16, and 80 weeks respectively (Figure 3A-C). Proteins that were uniquely up-regulated at 8 weeks and 16 weeks were enriched for RNA processing and ribosome/translation-associated gene ontology (GO) terms respectively (Figure 3D, E). 909 proteins were differentially expressed at 80 weeks only and were highly enriched for GO terms associated with mitochondria and metabolic processes ( Figure 3F). 228 proteins were commonly differentially expressed at all three ages ( Figure 3G) which were enriched for GO terms associated with muscle function, sarcolemma, extracellular matrix, and the DAPC ( Figure 3H).
TMT ratios for all dystrophic (n=18) and all WT (n=9) animals were further pooled, and statistically significant differences between these groups tested by t-test. The purpose of this analysis was to identify proteins that are differentially expressed in dystrophic muscle independent of mutation type or age (with the added benefit of increased statistical power). Of and has been previously shown to interact with α-dystroglycan (DAG1) (74) suggesting that down-regulation of EGFLAM may be a consequence of DAPC disruption.
Protein expression ratios from the pooled analysis were analyzed using ingenuity pathway analysis (IPA) in order to identify perturbed canonical pathways (Figure 4B, C) and predicted upstream regulators ( Figure 4D) in dystrophic muscle. This analysis identified multiple findings consistent with established features of dystrophic pathology (e.g. calcium signaling, inflammasome pathway, nitric oxide signaling, and TNF signaling). Furthermore, many affected canonical pathways and predicted upstream regulators were identified with no known association with DMD pathology (e.g. NRF2-mediated oxidative stress response and ATM signaling, KDM5A, and RICTOR, Figure 4B, C). Mitochondrial Dysfunction was the most significantly affected canonical pathway, consistent with our previous study (39). Notably, the increased depth of proteome coverage enabled the detection of protein expression changes that were not possible in our previously published analysis (39). For example, multiple protein components of Complex IV were down-regulated in dystrophic muscle in addition to the other four complexes of the electron transport chain which we reported previously (39). Metabolic pathways associated with the mitochondria were similarly down-regulated (i.e. TCA Cycle II, Oxidative Phosphorylation, Fatty Acid β-oxidation I).
In order to identify the most important proteins contributing to dystrophic pathology, differentially expressed proteins were filtered to include only those with a fold change greater sushi repeat-containing protein (SRPX), prolargin (PRELP), the serine protease HTRA1, the annexins: ANXA1 and ANXA4, and the S100 proteins: S100A4 and S100A11. Conversely, fibromodulin (FMOD), fibrillin-2 (FBN2), and tenascin C (TNC) were all down-regulated in dystrophic muscle. Interestingly, two thrombospondin genes were differentially expressed, although in opposite directions, with THBS3 being up-regulated and THBS4 down-regulated in dystrophic muscle. Similarly, the serpins: SERPINB1 and SERPINF1, were up-and down-regulated respectively. In contrast, with the muscle-associated proteins described above, some extracellular matrix factors were perturbed at the 80-week age only or exhibited a progressive increase in expression with age (i.e. COL3A1, MYOC, SRPX, BGN, THBS3, and HTRA1).
Together these findings are consistent with widespread matrix remodeling and later-onset fibrotic damage (Figure 5B).  Figure 5D). As with the extracellular matrix-associated proteins, the immune-response and lipid metabolism categories contained some proteins that were differentially expressed at all ages, and some that were perturbed to a greater extent in the 80 week-old samples.

Proteomic changes associated with the progression of dystrophic pathology
We next sought to identify proteins that were differentially expressed between dystrophic and WT samples, and which exhibited age-associated changes in expression (and therefore pathological progression). One-way analysis of variance was performed on the dystrophic samples in order to identify significant differences (adjusted P<0.01) between 8, 16, and 80 week-old pooled dystrophic mice, and this list was then filtered using the list of proteins that were found to be differentially expressed in any of the analyses performed above. The overlapping 1,196 proteins were then classified into 5 k-clusters based on their expression patterns. The character of each cluster was then illustrated by line graphs of the mean expression of all proteins ( Figure 6B) and heatmaps ( Figure 6C). Gene list enrichment analysis was performed in order to assign biological meaning to each cluster.
Cluster 1 contained proteins that progressively declined with age but were generally elevated in dystrophic muscle throughout. This cluster was enriched for GO terms associated with focal adhesion and mRNA splicing. Conversely, Cluster 4 contained proteins that were progressively up-regulated with age in both WT and dystrophic muscle. In general, expression of proteins in this cluster was down-regulated in dystrophic muscle, although a subset of proteins (marked with † in Figure 6C) Cluster 5 was particularly interesting, as it contained proteins that were up-regulated in 80 week-old dystrophic mice, and so are therefore likely associated with more advanced pathological progression. Proteins in Cluster 5 were enriched for the GO terms: extracellular matrix (Figure 6D), endoplasmic reticulum membrane (Figure 6E), and enzyme regulator activity ( Figure 6F). The effect sizes for protein expression changes were highest for the extracellular matrix associated changes, which included several proteins discussed above (i.e. In summary, the proteins in Clusters 1 and 4 might be considered factors that are differentially expressed with aging, but to a greater magnitude in dystrophic muscle. Conversely, Clusters 3, 4 and 5 represent proteins that are progressively differentially expressed in dystrophic muscle with more advanced pathology.

Pathway analysis in dystrophic muscle throughout pathological progression
Dystrophic vs WT protein expression ratios at each age were analyzed separately using IPA in order to identify changes in canonical pathway and upstream regulator status associated with disease progression. Overall results were similar to those described above where statistical power was greater on account of larger sample sizes ( Figure 4C) Similar findings were observed when considering predicted upstream regulators at each age ( Figure S12). Many of the predicted regulators identified were common to the pooled analysis described above (Figure 4D) TRIM24, and CPT1C which were inhibited with age (among many others) ( Figure S12).
Interestingly, a number of pathways and upstream regulators were differentially modulated to a lesser extent at the 16 week age, relative to the 8 and 80 week ages (Figures S11, S12). Such a pattern of expression is also apparent for some proteins in the heatmaps shown in (Figures   5B-D and 6C, Cluster 2). These patterns are in accordance with the period of stabilization observed in the mdx mouse following the 'crisis' period which occurs between 3 and 8 weeks of age (76).
Together these analyses have identified disease progression-associated changes in activation state for canonical pathways and upstream regulators in dystrophic muscle. Findings from the pooled analysis (Figure 4) were generally also consistent in the analyses performed separately at each age, despite lower statistical power in the latter (Figures S10, S11, S12).

CAV3, MVP, and PAK1 are up-regulated in dystrophic muscle
Three proteins were selected for further validation of the proteomics data:  (Figure 7A, B). Similar results were obtained in three independent blots (one representative blot is shown for each protein, Figure 7B). Expression patterns were comparable between HiRIEF-LC-MS/MS and Western blot experiments (Figure 7B), thereby validating the mass spectrometry data using an orthogonal protein quantification methodology.
The expression of the corresponding mRNA transcripts was determined in parallel, which mirrors the protein expression data in the case of Mvp and Pak1, consistent with their transcriptional up-regulation in dystrophic muscle, although some changes between WT and the dystrophic groups did not reach statistical significance at the P<0.05 level (Figure 7C). In contrast, Cav3 mRNA expression was not significantly increased in mdx and mdx52 samples compared to the WT at 8 and 80 weeks of age.

MVP is up-regulated during, but dispensable for, myogenic differentiation
We next sought to further investigate the function of MVP in the commonly used C2C12 murine myoblast cell line. C2C12 cells can be propagated as undifferentiated myoblasts, but upon several days culture in low serum differentiation medium (DM) these cells fuse into syncytial myotubes (Figure 8A). Mvp mRNA and MVP protein were expressed at low levels in proliferating myoblasts and progressively up-regulated during myogenic differentiation ( Figure 8B, C). However, depletion of MVP expression by RNA interference did not affect myogenic differentiation (Figure 8D, E) despite high levels of knockdown (Figure 8F, G).
These data suggest that MVP may play a role in, but is dispensable for, myogenic differentiation/muscle regeneration.

Discussion
The study of the proteomic alterations which underlie diseases such as DMD remains important for understanding disease pathology, identifying novel therapeutic targets, and discovering biomarkers of disease progression or response to therapy. In this study, we have utilized massspectrometry-based profiling to characterize the proteomic signature of dystrophic muscle in two different mouse models of DMD, and at different stages of the disease. Despite the technical challenges associated with proteomics analysis in skeletal muscle, we report quantification of 4,974 proteins across all nine conditions in triplicate (i.e. WT, mdx, and mdx52 at 8 weeks, 16 weeks, and 80 weeks of age) (Figure 1), the highest number of quantified proteins in dystrophic muscle described to date.
We selected the TA for proteomic analysis for several reasons, (i) we have previously generated a wealth of comparable proteomics/transcriptomics/miRNomics data in this muscle (39,61), (ii) the TA is accessible for intramuscular injection of therapeutics and toxicants allowing for comparison with data derived from such studies, and (iii) experimental restoration of dystrophin restoration in this muscle is relatively straight forward (56,57,61,77). The diaphragm muscle of the mdx mouse exhibits more severe degeneration (78) and so high resolution analyses in this tissue may reveal additional insights into dystrophic pathology.
Others have performed proteomics analyses in dystrophic diaphragm, although at lower depth than described here (72,79).
Initial analyses aimed to identify proteomic changes that might explain the differences in muscle regeneration phenotype observed in the TA when comparing between the mdx and mdx52 strains at 8 and 16 weeks of age (32). However, our data suggest that the skeletal muscle proteomes of these two dystrophic mouse models are very highly similar (Figures 2, S6, S7).
Importantly, much of the proteome remains invisible on account of the peptide masking effect (49), failure to consistently detect low abundance protein-derived peptides in all samples, or peptides from some proteins falling outside the narrow isoelectric point range used for prefractionation. Based on an update of our previous calculations of protein-coding transcript detection (39), we estimate that ~35% of the proteome was detected in the analyses described here. As such, putative proteins which are differentially expressed between mdx52 and mdx mice may be invisible to our analysis for reasons independent of statistical power.
Alternatively, expression changes in specific subpopulations of cells (e.g. infiltrating immune cells) or subsets of myofibers (e.g. regenerating fibers) may not be detectable in the context of unchanged protein expression in the bulk population. It is therefore possible that the differences between the mdx and mdx52 models may be explained by expression changes in proteins which we could not be detected using this methodology. Future proteomic studies should aim to understand dystrophy-associated expression changes in distinct cell/fiber subtypes.
The pooling of expression data from the mdx and mdx52 strains allowed us to identify key proteins associated with dystrophic pathology, irrespective of mutation (Figure 3, 4). Pathway analysis was utilized in an effort to understand the biological relevance of differential protein expression. Mitochondrial disruption was identified by both IPA and gene list enrichment across multiple analyses (Figures 3, 4, S10, and 6C Clusters 2 and 3). A reduction in the number of mitochondria in dystrophic muscle is a possible explanation for these findings and is further supported by the down-regulation of the protein components of all five electron transport complexes. Furthermore, the analysis of protein expression across ages suggests that this perturbation of mitochondrial biology worsens with pathological progression (Figure 3,   6C). Nevertheless, Mitochondrial Dysfunction was still observed in the 8 week-old dystrophic samples (Figure 3, S10). Percival (Figure 4, S10C, S12). (Notably, down-regulation of PGC-1α protein itself has been reported in the vastus lateralis muscle of the dystrophic GRMD dog (46)). Overlap between differentially expressed proteins enriched in PPAR, PGC-1α, PGC-1β, and TFAM was minimal (data not shown), suggesting bona fide perturbation of these distinct regulators as opposed to enrichment due to high numbers of differentially expressed proteins that are common between gene lists. Down-regulation of PGC-1α activity, and the resulting loss in mitochondrial numbers/function, may therefore account for the observed inhibition of the canonical pathways associated with mitochondrial dysfunction, oxidative phosphorylation, fatty acid oxidation, and gluconeogenesis (82) (Figure 4). Multiple factors may contribute to down-regulation of PGC-1α. For example, AMPK (5' AMP-activated protein kinase) and SIRT1 activate PGC-1α by phosphorylation and deacetylation respectively (83), both of which were predicted to be down-regulated by upstream regulator analysis (Figure 4).
Several inflammation-associated canonical pathways and upstream regulators were predicted to be up-regulated, consistent with the well-described persistent inflammation that accompanies myonecrosis and regeneration in dystrophic muscle (84). These included tumor necrosis factor (TNF), interferon gamma (INFG, INF-γ) (85) and the pro-fibrotic transforming growth factor beta (TGFB1, TGF-β) (Figure 4). Pro-inflammatory cytokines are likely derived from the infiltrating immune cells. However, TNF and INFG activate the NF-κB (Nuclear factor kappa-light-chain-enhancer of activated B cells), a transcription factor which acts as a master regulator of inflammation, which in turn results in feed-forward activation of TNF and IFNG expression in the myofibers themselves. Activation of NF-κB is a well-described feature of dystrophic muscle (13,86,87) but was not identified as being up-regulated in our dataset, possibly as a consequence of limited proteome coverage. Conversely, the NF-κB inhibitor (NFKBIA, IκBα) was paradoxically predicted to be up-regulated. This is consistent with the reported auto-regulatory feedback loop by which NF-κB promotes the expression of its inhibitor (88). Interestingly, NF-κB negatively regulates PGC-1α via a direct binding interaction (89,90), and TNF has been shown to also suppress PGC-1α in a cardiac cell model which were all up-regulated in dystrophic muscle (Figures 4, S10, S11, S12) and are possible factors contributing to PI3K/AKT activation (94,95). ILK signaling was also identified by pathway analysis in our previous study in mdx TA (39), and others have similarly reported ILK activation in the mdx tissues (47,92). Additionally, there is some evidence that inflammatory cytokines such as TNF and IFNG can stimulate PI3K/AKT (96).
PI3K/AKT itself contributes to the activation of the NF-κB via the phosphorylation of IKK, which in turn inhibits IκBα by phosphorylation (92), and has been shown to directly inhibit PGC-1α via phosphorylation in liver cells (97). Taken together, these data point to interactions between metabolic, mitochondrial biogenesis, inflammatory, and muscle growth pathways in dystrophic muscle (Figure 9).
Notably, many of the regulatory proteins identified above have been explored as potential targets for pharmacological manipulation (Figure 9). For example, PGC-1α gain-of-function and pharmacological activation of PPARD have been shown to be beneficial in the mdx mouse (98), and reverse mitochondrial defects in mdx myoblasts (81), respectively. Activation of AMPK and SIRT1 (by AICAR (99) and resveratrol (100) respectively) has been shown to attenuate pathology in the mdx mouse, also a likely consequence of PGC-1α up-regulation.
Treatment with NF-κB inhibitors (such as CAT-1041 and edasalonexent) ameliorated dystrophic pathology in mdx mice and GRMD dogs (101) and has shown promise in a phase 1 clinical trial in DMD patients (102). Treatment with the anti-TNF antibody infliximab (brand name: Remicade) improved dystrophic pathology by reducing myonecrosis (103), and cardiac fibrosis (104) in dystrophic mice, although detrimental changes in heart function were observed attributable to inhibition of the PI3K/AKT pathway (104).
Pathway analysis with IPA is appealing as it takes expression fold changes into account, generates Z-score outputs for pathway activation state which are easily interpretable, allows for exploration of regulatory relationships, and includes the prediction of perturbed upstream regulators. However, any pathway analysis is necessarily dependent on the quality of its gene list annotations. As such, we have found that IPA frequently does not identify muscle associated pathways/regulators (data not shown). In contrast, multiple muscle-associated GO terms were identified using a less sophisticated gene list enrichment approach (55). This was particularly apparent in the list of the most differentially expressed proteins ( Figure 5A). We have therefore performed both types of analysis in this study.
A recent study by Capitanio et al. described a label free mass spectrometry-based proteomics analysis of vastus lateralis muscles biopsied from DMD and BMD patients, compared with healthy controls (n=3) (48). To determine whether the findings reported here could be recapitulated in human muscle we compared all proteins found to be differentially expressed in DMD or BMD relative to controls against all proteins found to be significantly altered in any of the dystrophic vs wild-type comparisons described in this study. 135 proteins were commonly identified between DMD/BMD patients and dystrophic mice, thereby providing independent evidence that many of the findings reported here are relevant to the situation in dystrophic patient muscle ( Figure S13A). Indeed, this level of overlap is perhaps surprising when considering the differences in organism, muscle type, mutation type, disease-stage progression, and the methodologies used. Conversely, 91 proteins uniquely identified in the human study, of these 48 were also detected in our datasets, but were not differentially expressed, with the remaining 43 proteins invisible in our analyses ( Figure S13B). 2,458 proteins uniquely identified as being differentially expressed in the dystrophic mouse samples, which emphasises the benefit of the high-resolution proteomics approach used in this study in terms of deep proteome coverage. Differentially expressed proteins that were common to dystrophic human and mouse muscle were enriched for GO terms associated with muscle structure, the cytoskeleton, and energy production ( Figure S13C) consistent with findings described above (Figure 3). Proteins that were uniquely differentially expressed in the human patient dataset were enriched for antioxidant activity, peroxidase activity, exocytosis, and vesicle structure and function ( Figure S13D). Notably the P-values for these enriched GO terms were relatively low. Proteins that were uniquely differentially expressed in our murine datasets were enriched for RNA binding, multiple metabolic processes, and the mitochondrion (Figure S13E) similar to findings described above (Figure 3). P-values for the enrichment of these GO terms were comparatively very high, which is to be expected when considering the greater number of proteins available for analysis.
A possible limitation of this study is the influence of batch effects, as experimental samples for each age were split between three separate pools of TMT-labelled peptides. This study design ensured that statistical tests performed within each age are not influenced by batch effects.
However, comparisons between ages may be subject to this technical source of variation, although there is evidence that such effects are minimal in our data. Firstly, the normalization of TMT ratios to the bridge sample minimizes batch effects to some extent, and the proteomics performance for the bridge sample was highly similar for each TMT10 set ( Figure S3).
Secondly, comparing TMT ratios across all samples for known proteins that are differentially expressed in dystrophic muscle shows minimal differences between ages/TMT10 sets ( Figure   S5), and the majority of proteins are not changed over time (Figure 3, 4, S6, File S1). Thirdly, proteins/pathways that are altered with age are consistent with established features of dystrophic pathology (such as the accumulation of fibrotic damage) (Figure 6, S10, S11, S12).
This study has highlighted multiple proteins of interest, of which we selected MVP for further investigation. MVP is the major constituent of vault particles, ribonucleoprotein structures (the function of which is poorly understood) (105). MVP was up-regulated during, but dispensable for, myogenic differentiation (Figures 7, 8) suggesting that MVP up-regulation may be associated with muscle regeneration in dystrophic muscle. Alternatively, MVP may reflect a different facet of dystrophic pathology, as this protein has been shown to be elevated in response to serum-starvation-induced apoptotic stress (106). Notably, the other protein components of vault particles (i.e. TEP1 and PARP4) were similarly up-regulated in dystrophic muscle, although to a lesser extent, suggesting an increase in the number of vaults may explain these data. Recent evidence has shown that vault particles can act as scaffolds and are involved in cell survival signaling (107,108). Moreover, they have been reported to regulate growth and survival of respiratory smooth muscle cells (108). To our knowledge, this is the first study demonstrating an association between MVP/vault particles and dystrophic pathology, which is deserving of further investigation.
Through the use of two dystrophic mouse models and the inclusion of different disease time points, this study provides a wider perspective of, and offers new insights into, the pathological mechanisms involved in Duchenne muscular dystrophy. The data presented here describe a wealth of novel differentially expressed proteins, perturbed biological pathways, and predicted upstream regulators. This dataset will constitute a valuable resource for the DMD research community, and for those studying the effects of aging on the WT muscle proteome. scientific discussion. We acknowledge support from SciLifeLab proteogenomics core facility. Prepared the first draft: TLEvW, TCR.

Data availability
Protein expression data and statistical analysis are summarized in File S1. All other data relating to this manuscript are freely available on request from the authors. The mass spectrometry raw proteomics data have been deposited to the ProteomeXchange Consortium via the JPOST partner repository with the dataset identifier PXD017169 (JPST000734).    Differentially expressed proteins and pathway analysis in dystrophic muscle independent of age or mutation type.
All TMT ratios for the mdx and mdx52 samples (Dystrophic, n=18) were pooled and differentially expressed proteins determined relative to WT controls (n=9) independent of age      Crosstalk between metabolic, inflammatory and muscle growth pathways in dystrophic muscle.
Upstream regulator and canonical pathway analyses were integrated in order to generate an explanatory schema of processes occurring in dystrophic muscle. Drugs with the potential to treat DMD are shown in purple. Activation of NF-κB was not predicted based on our data but has been well-described in dystrophic muscle. ILK Signaling was enriched in dystrophic muscle, although the analysis did not determine an activation state.