Abstract

Oculopharyngeal muscular dystrophy (OPMD) is an adult-onset disorder characterized by ptosis, dysphagia and proximal limb weakness. Autosomal-dominant OPMD is caused by a short (GCG)8–13 expansions within the first exon of the poly(A)-binding protein nuclear 1 gene (PABPN1), leading to an expanded polyalanine tract in the mutated protein. Expanded PABPN1 forms insoluble aggregates in the nuclei of skeletal muscle fibres. In order to gain insight into the different physiological processes affected in OPMD muscles, we have used a transgenic mouse model of OPMD (A17.1) and performed transcriptomic studies combined with a detailed phenotypic characterization of this model at three time points. The transcriptomic analysis revealed a massive gene deregulation in the A17.1 mice, among which we identified a significant deregulation of pathways associated with muscle atrophy. Using a mathematical model for progression, we have identified that one-third of the progressive genes were also associated with muscle atrophy. Functional and histological analysis of the skeletal muscle of this mouse model confirmed a severe and progressive muscular atrophy associated with a reduction in muscle strength. Moreover, muscle atrophy in the A17.1 mice was restricted to fast glycolytic fibres, containing a large number of intranuclear inclusions (INIs). The soleus muscle and, in particular, oxidative fibres were spared, even though they contained INIs albeit to a lesser degree. These results demonstrate a fibre-type specificity of muscle atrophy in this OPMD model. This study improves our understanding of the biological pathways modified in OPMD to identify potential biomarkers and new therapeutic targets.

INTRODUCTION

Oculopharyngeal muscular dystrophy (OPMD) is a late-onset autosomal dominant genetic disease, characterized by progressive eyelid drooping, swallowing difficulty and proximal limb weakness in the late stages of the disease. The poly(A)-binding protein nuclear 1 (PABPN1) gene is mutated in OPMD patients and contains an expanded GCG trinucleotide repeat within exon 1 (1). This trinucleotide expansion is translated into a polyalanine tract at the N-terminus of the PABPN1 protein; in OPMD patients, this tract contains 12–17 alanine repeats instead of 10 repeats. PABPN1 with an expanded polyalanine tract forms nuclear aggregates (2). Although PABPN1 is ubiquitously expressed, the clinical and pathological phenotypes are restricted to skeletal muscles in OPMD patients, especially the pharyngeal and cricopharyngeal muscles (dysphagia), and the levator palpebrae superioris muscle (ptosis of the eyelid) (3).

PABPN1 is a protein localized in nuclear speckles, which binds with high affinity to poly(A) tails of mRNAs. PABPN1 promotes the interaction between the poly(A) polymerase and the cleavage and polyadenylation specificity factor, and controls the length of the poly(A) tail during polyadenylation of mRNA (4–7). PABPN1 also contributes to the export of mRNA from the nucleus to the cytoplasm (8,9). The major pathological hallmark of OPMD is intranuclear inclusions (INIs) characterized by tubular filaments (2). It has previously been demonstrated that these INIs contain a large number of nuclear factors such as ubiquitin, subunits of the proteasome (10), molecular chaperones HSP70 and HSP40 (11,12), poly(A) RNA (10), proteins involved in mRNA processing and transport CUGBP1, SFRS3, FKBP1A, hnRNP A1 and A/B and poly(A) polymerase (13–15). The exact role of PABPN1 aggregation in OPMD is still under debate. At present, it is still not clear whether the INIs observed in OPMD skeletal muscles have a pathological or a protective function by acting as a cellular defence mechanism against abnormal proteins. Several studies have suggested a pathological function of INIs: (i) the INIs could play a major role by sequestering essential cellular components such as specific mRNAs (10), splicing or transcription factors (13,14), (ii) the frequency of INIs in nuclei of muscle fibres is correlated with the severity of the disease, with a frequency of 2–5% for heterozygous and 10% for homozygous patients (16) and (iii) the reduction of the INIs in a mouse model by doxycycline or trehalose (17,18) or using intrabodies in a drosophila model (19) improves muscle function. However, several studies have also suggested that the INIs might just be the result of a cellular defence mechanism and not the direct cause of the disease: (i) INIs are found both in affected and less-affected skeletal muscles, (ii) Tavanez et al. (12) has recently proposed that the expansion alters the protein conformation and changes the binding properties of interacting proteins independently of the formation of INIs, (iii) the polyalanine domain of PABPN1 is not essential for aggregate formation (15,20,21) and (iv) it has been suggested that the soluble form of the mutated PABPN1 is itself pathogenic, whereas the INIs would be a form of cellular protection (22,23).

In order to study the pathological mechanisms underlying OPMD, several in vitro models have been developed expressing an expanded PABPN1 transgene: transiently transfected COS-7 and HeLa cells (11,23,24), adenovirus-infected A549tTA cells (13) or stably transfected C2 cells (25). In parallel, different animal models have also been generated: a drosophila model expressing PABPN1 with a polyalanine extension of different lengths, resulting in a muscular dystrophy with abnormal wing posture (20), a nematode model expressing different lengths of expanded PABPN1 and showing muscle cell degeneration and abnormal mobility (22) and several mouse models expressing either ubiquitously (26,27) or muscle specifically (18) expanded PABPN1 leading to the formation of INIs (18,27,28). In the mouse model developed by Davies et al., a mutated version of PABPN1 with 17 alanines (expPABPN1) is expressed under the control of the human skeletal actin (HSA1) promoter, restricting the transgene expression to the striated muscle. Mice expressing the expPABPN1 transgene (A17.1) show a progressive muscle weakness and a progressive accumulation of INIs (18).

The aim of the present study was to gain insights into the different physiological pathways affected in OPMD muscles by performing both a general transcriptomic analysis and a detailed phenotypic characterization of the skeletal muscle of A17.1 mice compared with wild-type (WT) mice at different time points. We have observed that the muscle-restricted expression of the expPABPN1 transgene induces considerable gene expression deregulation among which genes associated with muscle atrophy were particularly affected. Functional and histological analysis of the skeletal muscle of this mouse further confirmed a severe muscular atrophy associated with a reduction in muscle strength. Interestingly we showed that this muscular atrophy is restricted to fast glycolytic fibres, containing a large number of INIs, while oxidative fibres are spared, and contain less INIs. These results suggest a fibre-type specificity of muscle atrophy in this OPMD model, together with a less specific presence of INIs.

RESULTS

Gene expression profiling in muscle from mice expressing expPABPN1

To gain insight into molecular mechanisms involved in OPMD, we performed a transcriptomic analysis on skeletal muscle from A17.1 mice expressing an expanded form of PABPN1 with 17 alanines (expPABPN1). Davies et al. (18,29) previously described that these A17.1 mice show progressive formation of aggregates and progressive muscle weakness from approximately 18 weeks of age, whereas A10.1 mice expressing WT PABPN1 were indistinguishable from WT mice (29). By immunohistochemistry (Fig. 1A), we confirmed that, in A17.1 mice, the number of nuclei containing PABPN1 increases with age. At 6 weeks (T1), 8% of the nuclei contained aggregates, and this number progressively increased to 15% at 18 weeks (T2) and 30% at 26 weeks (T3) (Fig. 1B). Thus aggregation of expPABPN1 starts at a very early age, suggesting that potentially earlier muscle dysfunction may occur prior to the onset of muscle weakness symptoms observed from 18 weeks of age (18,29).

Figure 1.

(A) The KCl-insoluble nuclear aggregates containing expPABPN1 (green) were detected by immunostaining on skeletal muscle cryosections from A17.1 mice. The sections of WT mice did not show any KCl-insoluble aggregates. (red, dystrophin; blue, nuclei; green, PABPN1; magnification ×400.) (B) The percentage of nuclei containing PABPN1 aggregates was determined on skeletal muscle (TA) cryosections from 6 (T1), 18 (T2) or 26 (T3) weeks old A17 mice (n = 3 per time point with 250–350 fibers counted per muscle; the percentage of aggregates in T1 and T2 is significantly lower when compared with T3: T1 versus T2 **P < 0.01, T2 versus T3 ***P < 0.001).

Figure 1.

(A) The KCl-insoluble nuclear aggregates containing expPABPN1 (green) were detected by immunostaining on skeletal muscle cryosections from A17.1 mice. The sections of WT mice did not show any KCl-insoluble aggregates. (red, dystrophin; blue, nuclei; green, PABPN1; magnification ×400.) (B) The percentage of nuclei containing PABPN1 aggregates was determined on skeletal muscle (TA) cryosections from 6 (T1), 18 (T2) or 26 (T3) weeks old A17 mice (n = 3 per time point with 250–350 fibers counted per muscle; the percentage of aggregates in T1 and T2 is significantly lower when compared with T3: T1 versus T2 **P < 0.01, T2 versus T3 ***P < 0.001).

In order to identify the biological pathways that are initially deregulated, we carried out transcriptomic analyses on the skeletal muscle from 6-week-old mice (T1), when there are no obvious signs of muscle weakness, as well as from 18 (T2) and 26 weeks (T3) when the A17.1 mice are showing progressive muscle weakness. RNA expression arrays were generated from WT and A17.1 RNA isolated from quadriceps muscles, which were hybridized to Illumina Bead array v.1 containing 46 632 unique probe identifiers. After normalization, the quality of the microarray hybridization was evaluated with the principal component analysis (PCA) (30,31). For all three time points (T1-T3), PCA plots showed that mice with the same genotype (WT or A17.1) cluster together indicating that most variations in the arrays could be attributed to the genotype (Fig. 2A; component 1). A weaker association was found with the second component representing technical variations. The clustergrams representing hierarchical clustering for each time point (Supplementary Material, Fig. S1) further demonstrated that the differences in the distribution of gene expression intensities between muscle samples from WT and A17.1 mice were due to changes in the individual gene expression levels between groups rather than non-specific variations between samples. These results indicate that the gene expression changes between A17.1 and WT mice can be classified based on their genotype.

Figure 2.

Transcriptomic study in quadriceps muscles of A17.1 and WT mice at 6, 18 and 26 weeks. (A) PCA plots for each time point data sets. A17.1 and WT samples are represented with blue and red dots, respectively. (B) Venn diagram of the deregulated genes using the unbiased cut-off P-value of 0.05, showing the number of A17.1 deregulated genes in each time point and the overlapping genes between two or three time points.

Figure 2.

Transcriptomic study in quadriceps muscles of A17.1 and WT mice at 6, 18 and 26 weeks. (A) PCA plots for each time point data sets. A17.1 and WT samples are represented with blue and red dots, respectively. (B) Venn diagram of the deregulated genes using the unbiased cut-off P-value of 0.05, showing the number of A17.1 deregulated genes in each time point and the overlapping genes between two or three time points.

Subsequently, A17.1-deregulated genes were defined with a cut-off P-value of 0.05 and false discovery rate (FDR) corrected. The majority of up- or down-regulated genes were found in the 6-week-old mice (3220 and 3122, respectively). The total amount of either up- or down- regulated genes was gradually reduced at T2 (1910 and 1839, respectively) and T3 (2263 and 1866, respectively) (Fig. 2B). This observation indicates that overexpression of the expPABPN1 gene leads to considerable changes in the expression of a large number of genes. More importantly, most of the A17.1 deregulated genes overlapped between two or three time points (T1 61%; T2 86.9%; T3 80.9%, Fig. 2B), with a similar ratio between the up- or down-deregulated genes at all time points, indicating that overexpression of expPABPN1 does not lead to preferential transcriptional up- or down-regulation in the A17.1 mouse.

Since a massive gene deregulation was found in the A17.1 mice, rather than taking a gene-by-gene analysis approach, we searched for the biological pathways that were significantly affected in this OPMD mouse model. To determine the gene ontology (GO) categories that were significantly associated with the expPABPN1 overexpression genotype, we used the global test analysis (32). Significant GO categories were selected with the adjusted P-value of <0.05 corrected with FDR. Next, the significance of each GO terms was evaluated using an enrichment analysis, which calculates the significance of each cluster based on the proportion of differentially expressed genes that contributes to the respective cluster. A list of biological GO categories that are significantly deregulated in the A17.1 mice was created using DAVID (33,34), revealing a broad range of deregulated biological processes in the A17.1 mice (Table 1 and Supplementary Material, Table S1). We identified transcriptional deregulation of genes involved in mRNA processing (GO:0006397), cell cycle (GO:0007049), the ubiquitin–proteosome pathway (GO:0006511 and GO:0016567), protein transport (GO:0015031) and the mitochondria (GO:0007005), corroborating a previous transcriptome analysis in an OPMD cell model (13). We also found a significant deregulation of apoptosis (GO:0006915), confirming the cell death previously described in this mouse model (18,29) and in a cellular model (35). Importantly, we found a significant deregulation of GO categories that affect muscle biology.

Table 1.

Most significant A17.1 deregulated biological processes GO terms. Sorting is according to P-value

ID GO term P-value Genes (array—all) Deregulated genes % Deregulated genes 
GO:0051169 Nuclear transport 1.35E − 08 94 46 49 
GO:0009056 Catabolic process 1.41E − 08 872 361 41 
GO:0015031 Protein transport 1.60E − 08 591 250 42 
GO:0045859 Regulation of protein kinase activity 1.68E − 08 132 50 38 
GO:0006796 Phosphate metabolic process 1.69E − 08 804 313 39 
GO:0006950 Response to stress 1.77E − 08 955 289 30 
GO:0006457 Protein folding 1.87E − 08 107 47 44 
GO:0006397 mRNA processing 1.90E − 08 219 111 51 
GO:0007049 Cell cycle 1.93E − 08 615 197 32 
GO:0050790 Regulation of catalytic activity 1.96E − 08 331 114 34 
GO:0006915 Apoptosis 2.03E − 08 647 225 35 
GO:0051276 Chromosome organization and biogenesis 2.33E − 08 319 135 42 
GO:0007517 Muscle development 2.49E − 08 179 73 41 
GO:0009628 Response to abiotic stimulus 2.60E − 08 185 60 32 
GO:0007005 Mitochondrion organization 2.68E − 08 60 24 40 
GO:0006461 Protein complex assembly 2.89E − 08 164 66 40 
GO:0010608 Posttranscriptional regulation of gene expression 2.27E − 07 95 45 47 
GO:0006511 Ubiquitin-dependent protein catabolic process 2.27E − 07 451 215 48 
GO:0016567 Protein ubiquitination 1.88E − 05 53 26 49 
GO:0006412 Translation 9.69E − 03 273 139 51 
GO:0042692 Muscle cell differentiation 9.80E − 03 75 31 41 
GO:0048666 Neuron development 1.31E − 02 276 83 30 
ID GO term P-value Genes (array—all) Deregulated genes % Deregulated genes 
GO:0051169 Nuclear transport 1.35E − 08 94 46 49 
GO:0009056 Catabolic process 1.41E − 08 872 361 41 
GO:0015031 Protein transport 1.60E − 08 591 250 42 
GO:0045859 Regulation of protein kinase activity 1.68E − 08 132 50 38 
GO:0006796 Phosphate metabolic process 1.69E − 08 804 313 39 
GO:0006950 Response to stress 1.77E − 08 955 289 30 
GO:0006457 Protein folding 1.87E − 08 107 47 44 
GO:0006397 mRNA processing 1.90E − 08 219 111 51 
GO:0007049 Cell cycle 1.93E − 08 615 197 32 
GO:0050790 Regulation of catalytic activity 1.96E − 08 331 114 34 
GO:0006915 Apoptosis 2.03E − 08 647 225 35 
GO:0051276 Chromosome organization and biogenesis 2.33E − 08 319 135 42 
GO:0007517 Muscle development 2.49E − 08 179 73 41 
GO:0009628 Response to abiotic stimulus 2.60E − 08 185 60 32 
GO:0007005 Mitochondrion organization 2.68E − 08 60 24 40 
GO:0006461 Protein complex assembly 2.89E − 08 164 66 40 
GO:0010608 Posttranscriptional regulation of gene expression 2.27E − 07 95 45 47 
GO:0006511 Ubiquitin-dependent protein catabolic process 2.27E − 07 451 215 48 
GO:0016567 Protein ubiquitination 1.88E − 05 53 26 49 
GO:0006412 Translation 9.69E − 03 273 139 51 
GO:0042692 Muscle cell differentiation 9.80E − 03 75 31 41 
GO:0048666 Neuron development 1.31E − 02 276 83 30 

Since the A17.1-deregulated GO categories are biologically very broad and since OPMD affects muscle cells, we next used the literature to map significant biological concepts that would be muscle related. We assumed that the subgroup of overlapping deregulated genes common to the three time points is strongly associated with the disease aetiology, and therefore selected this subgroup for a literature-aided mapping of biological concepts using Anni 2.0 (36). Out of the 2336 overlapping deregulated genes, only 1679 genes were recognized by Anni 2.0 (Supplementary Material, Table S2). Among these, 481 deregulated genes (28.5%) were found to be highly associated with the terms ‘muscle atrophy’ or ‘skeletal muscle atrophy’ (Supplementary Material, Table S2), suggesting that muscle atrophy may already be triggered in the A17.1 mouse at 6 weeks.

To validate the transcriptome analysis by quantitative PCR, we selected 10 genes from the muscular atrophy association list using both >1.3-fold change and high P-values criteria. RNA isolated from quadriceps of 6-week-old WT or A17.1 mice were used for the validation study. For each gene, the expression level was compared between the microarray and the quantitative PCR (Fig. 3). After normalization to the WT control, a similar change in expression level was observed for each gene analysed, demonstrating that our microarray analysis is valid.

Figure 3.

Validation of A17.1 deregulated expression level of selected genes in skeletal muscle of A17 mice. Histograms indicate the expression levels normalized to that measured in the WT mice. Values measured by quantitative RT–PCR (greys bars) or microarray (black bars) are means ± standard deviations for n = 5–6 mice per group (*P < 0.05).

Figure 3.

Validation of A17.1 deregulated expression level of selected genes in skeletal muscle of A17 mice. Histograms indicate the expression levels normalized to that measured in the WT mice. Values measured by quantitative RT–PCR (greys bars) or microarray (black bars) are means ± standard deviations for n = 5–6 mice per group (*P < 0.05).

As muscle weakness in the A17.1 mice is progressive (18), we applied mathematical modelling for progressiveness on the A17.1-deregulated genes. A linear regression model was generated using the Limma model in R (37) and was applied to all of the genes in the array. A total of 410 genes were identified as candidates for this progression. Subsequently, these 410 genes were applied in Anni 2.0 to find an association with the terms ‘muscle atrophy’ and ‘skeletal muscle atrophy’. Of the 410 candidates genes, only 168 genes were available for Anni analysis. Among these 163 genes, 63 (38.6%) were highly associated with muscle atrophy in the biomedical literature (Supplementary Material, Table S2). This analysis strongly suggests that the deregulation of muscle mass is progressive in the A17.1 mice. To confirm this analysis, eight genes were selected using the fold change criteria and their expression profiles over time were plotted. The progression plots of fold change showed a linear positive or negative progression for all selected up- or down-regulated genes, therefore validating the mathematical modelling (Fig. 4).

Figure 4.

Validation of the mathematical model for progression analysis. Expression plots of individual selected genes showed linear progression. The fold change is calculated from the microarray analysis. Graphs are sub-grouped according to up- or down-regulated genes and positive or negative linear regression.

Figure 4.

Validation of the mathematical model for progression analysis. Expression plots of individual selected genes showed linear progression. The fold change is calculated from the microarray analysis. Graphs are sub-grouped according to up- or down-regulated genes and positive or negative linear regression.

Muscle atrophy in A17.1 mice

Since the transcriptomic study indicates muscle atrophy in the A17.1 mice, we performed a detailed analysis of the skeletal muscles of the A17.1 mice over time. Using the grip test, it was previously shown that the A17.1 mice develop a progressive muscle weakness with a significant decrease in strength compared with WT mice from 18 weeks of age (18,29), whereas A10.1 mice expressing WT PABPN1 were indistinguishable from WT mice (29). In order to further analyse the consequences of the expPABPN1 expression on the physiological function of skeletal muscle, we measured the contractile properties of the tibialis anterior (TA) skeletal muscle of A17.1 mice at 18 and 26 weeks of age when compared with age-matched WT littermates. The maximal absolute force of the TA of A17.1 transgenic mice was significantly reduced by 36% at 18 weeks and 48% at 26 weeks when compared with WT mice (Fig. 5A). The mass of the TA muscle of A17.1 mice was progressively reduced by 27% at 18 weeks and 39% at 26 weeks when compared with WT mice (Fig. 5B). This progressive reduction in muscle mass was observed from 6 weeks of age (20% reduction at 6 weeks, data not shown) and was also observed in other skeletal muscles such as the quadriceps and the gastrocnemius (data not shown). This effect is specifically due to the overexpression of expPABPN1 since A10.1 mice expressing WT PABPN1 at higher level than A17.1 mice did not show a similar reduction in TA muscle mass (data not shown). We next calculated the specific force of the TA muscles of A17.1 and WT mice by normalizing the maximal (absolute) force to the muscle mass. This measure showed that the specific force of the TA of A17.1 mice was significantly reduced by 14% at 18 weeks and 20% at 26 weeks when compared with WT mice (Fig. 5C). Both decreases in muscle mass and in specific force participate to the decrease in absolute maximal force.

Figure 5.

Measurements of the weight and functional performance of skeletal muscle in WT and A17.1 mice at 18 and 26 weeks of age (n = 6 per group). (A) The maximal force produced by the TA muscle was determined in WT and A17.1 mice (***P < 0.001). (B) The mass of the TA muscle was measured in A17.1 and WT mice (*P < 0.05; ***P < 0.001). (B) The specific force (N/g) for the TA muscles of A17.1 and WT mice was calculated by dividing the maximal absolute force by the muscle mass (*P < 0.05).

Figure 5.

Measurements of the weight and functional performance of skeletal muscle in WT and A17.1 mice at 18 and 26 weeks of age (n = 6 per group). (A) The maximal force produced by the TA muscle was determined in WT and A17.1 mice (***P < 0.001). (B) The mass of the TA muscle was measured in A17.1 and WT mice (*P < 0.05; ***P < 0.001). (B) The specific force (N/g) for the TA muscles of A17.1 and WT mice was calculated by dividing the maximal absolute force by the muscle mass (*P < 0.05).

This reduced specific force demonstrates that there is both a qualitative change as well as an additional pathological process occurring in the skeletal muscle. Whereas immunostaining on muscle sections did not reveal any obvious modifications of the dystrophin-associated glycoprotein complex (data not shown), an haematoxylin-eosin (H&E) staining revealed an increased number of centrally nucleated fibres in A17.1 when compared with WT mice (Fig. 6A). In addition, a Sirius red staining revealed a more pronounced endomysial fibrosis in A17.1 mice when compared with WT mice (Fig. 6B), which could potentially explain the reduced specific force. This muscle weakness could also result from a modified mitochondrial function, as this pathway was shown to be deregulated in the transcriptomic data (Table 1). Mitochondrial ATP is generated via oxidative phosphorylation through the combined action of five enzyme complexes. Citrate synthase levels were similar in WT and A17.1 TA muscle, suggesting that the total amount of mitochondria is preserved; however, assessment of mitochondrial respiratory chain enzyme activity showed a decreased activity in complex I (NADH: ubiquinone reductase) in A17.1 when compared with WT mice (Fig. 6C). In contrast, complex II–III (succinate:cytochrome c reductase) and complex IV (cytochrome c oxidase) activities were not decreased when compared with control levels (data not shown). These data suggest a mitochondrial dysfunction, which could result in muscle contractile defects and therefore also participate to the decrease in specific force.

Figure 6.

(A) Centrally nucleated fibers were determined on transversal sections of WT (n = 3) and A17.1 (n = 4) TA muscles of 26 weeks old mice following an hematoxylin/eosin staining. For each section, more than 800 fibers were counted from four random areas. The results represent the percentage of centrally nucleated fibers (*P < 0.05). (B) Sirius Red staining of transversal sections of WT (n = 3) and A17.1 (n = 4) TA muscles of 26 weeks old mice (**P < 0.01). (C) Citrate synthase (CS) activity and mitochondrial complex I activity measurement on WT and A17.1 muscle (n = 6 per group) from 26-week-old mice. The activity of complex I is expressed in nmol/min/ml and then normalized relative to citrate synthase as an indicator of mitochondrial content (**P < 0.01).

Figure 6.

(A) Centrally nucleated fibers were determined on transversal sections of WT (n = 3) and A17.1 (n = 4) TA muscles of 26 weeks old mice following an hematoxylin/eosin staining. For each section, more than 800 fibers were counted from four random areas. The results represent the percentage of centrally nucleated fibers (*P < 0.05). (B) Sirius Red staining of transversal sections of WT (n = 3) and A17.1 (n = 4) TA muscles of 26 weeks old mice (**P < 0.01). (C) Citrate synthase (CS) activity and mitochondrial complex I activity measurement on WT and A17.1 muscle (n = 6 per group) from 26-week-old mice. The activity of complex I is expressed in nmol/min/ml and then normalized relative to citrate synthase as an indicator of mitochondrial content (**P < 0.01).

The reduction in muscle mass and force together with the transcriptomic data suggests that the expression of expPABPN1 triggers muscular atrophy. To further confirm this hypothesis, we performed a detailed histological analysis of the TA muscle of A17.1 mice when compared with WT mice at 26 weeks of age. We observed a 30% reduction in the maximal cross-sectional area (CSA) of the TA in A17.1 transgenic mice when compared with their age-matched WT littermates (Fig. 7A). On muscle sections that generated the maximal CSA, we subsequently analysed individual fibre CSA using an anti-laminin antibody to delimit the muscle fibres. When the frequency distribution of the fibres was plotted according to their CSA (Fig. 7B), a shift was observed from the large towards the small size of muscle fibres in the A17.1 mice. The CSA was reduced by ∼30% (189 µm2 for WT mice and 132 µm2 for A17.1 mice), whereas there was no change in the total number of fibres between WT and A17.1 mice (Fig. 7C). Interestingly, the reduction in muscle size was not associated with a decrease in myonuclear number (Fig. 7D). Overall, these results confirm muscular atrophy, defined as a decrease in cell size by loss of organelles, cytoplasm and proteins (38). This reduction in muscle mass is due to an improper balance between protein synthesis and degradation, inducing a loss of total protein content in muscle fibres (39). The ubiquitin–proteasome pathway is activated during muscle atrophy and is involved in the breakdown of major contractile proteins (40,41). In particular, MuRF-1 and Atrogin-1, known as atrogenes, play a crucial role in the loss of muscle proteins and their expression is considered as specific atrophy markers (38,42). In the progression analysis (Fig. 4) and by quantitative RT–PCR (Fig. 3), we have shown that the atrogene MuRF-1 only was indeed up-regulated in A17.1 mice. We further confirmed that in the TA of 26-week-old A17.1 mice there was a persistence of this MuRF-1 mRNA up-regulation (Fig. 7E), mainly mediated by a down-regulation of the active phosphorylated form of PKB/Akt and a translocation of Foxo3A transcription factor to the nucleus (Supplementary Material, Fig. S2). We also measured proteasome activities (chymotrypsine-, trypsin- and caspase-like) in the TA muscle of 26-week-old mice and observed a significant increase in the chymotrypsin- and caspase-like activity in A17.1 mice, whereas the trypsin-like activity was not significantly increased (Fig. 7F). Altogether, these data confirm muscular atrophy in the A17.1 mice.

Figure 7.

Evaluation of the skeletal muscle atrophy at 26 weeks of age. (A) The maximal cross section area (CSA) of the TA muscle was measured for the TA of WT and A17.1 mice (n = 6 per group), ***P < 0.001. (B) The frequency of the cross-sectional area (CSA) of the muscle fibers was determined in the TA muscle from WT and A17.1 mice. The plotted lines represent the mean of three different muscles for each group (χ2 analysis performed on data sets, P < 0.001). (C) The total number of fibers per muscle was determined in the TA of WT and A17.1 mice and did not show any difference (n = 3 per group). NS, non-significant. (D) The number of nuclei per fiber on TA muscle section was similar in both A17.1 and WT mice (n = 4 per group with around 250–350 fibers counted per muscle). (E) MuRF1 mRNA expression in TA muscle of 26-week-old mice. Values measured by quantitative RT–PCR are means ± standard deviations for n = 5–6 mice per group, *P < 0.05. (F) Proteasome activity in TA muscle of 26-week-old mice. Ct-like, chimotrypsin-like; Tryp-like, Trypsin-like; Casp-like, Caspase-like. The results are expressed in F.U/min, **P < 0.01; n = 4 for WT and n = 6 for A17.1.

Figure 7.

Evaluation of the skeletal muscle atrophy at 26 weeks of age. (A) The maximal cross section area (CSA) of the TA muscle was measured for the TA of WT and A17.1 mice (n = 6 per group), ***P < 0.001. (B) The frequency of the cross-sectional area (CSA) of the muscle fibers was determined in the TA muscle from WT and A17.1 mice. The plotted lines represent the mean of three different muscles for each group (χ2 analysis performed on data sets, P < 0.001). (C) The total number of fibers per muscle was determined in the TA of WT and A17.1 mice and did not show any difference (n = 3 per group). NS, non-significant. (D) The number of nuclei per fiber on TA muscle section was similar in both A17.1 and WT mice (n = 4 per group with around 250–350 fibers counted per muscle). (E) MuRF1 mRNA expression in TA muscle of 26-week-old mice. Values measured by quantitative RT–PCR are means ± standard deviations for n = 5–6 mice per group, *P < 0.05. (F) Proteasome activity in TA muscle of 26-week-old mice. Ct-like, chimotrypsin-like; Tryp-like, Trypsin-like; Casp-like, Caspase-like. The results are expressed in F.U/min, **P < 0.01; n = 4 for WT and n = 6 for A17.1.

In order to further evaluate if we could locally reproduce this atrophic phenotype in the skeletal muscle of an adult WT mice, we overexpressed the expanded PABPN1 transgene using an adeno-associated virus (rAAV2/8-CAG-expPABPN1, Supplementary Material, Fig. 3A) injected into the TA of WT mice at 8 weeks of age. Three months post-injection, we confirmed the overexpression of expPABPN1 and the presence of expPABPN1 INIs only in the injected muscle fibres (Supplementary Material, Fig. 3B). Similar to what we measured in A17.1 mice, we observed a reduced muscle mass and reduced maximal force of the injected TA of WT mice when compared with the contralateral uninjected leg, leading to a slight but not significant reduction in the specific force (Supplementary Material, Fig. 3C). This result further confirms that the expression of expPABPN1 in mature muscle fibres induces an atrophic process.

Distinct phenotypes between oxidative and glycolytic fibre subtypes

Muscle is composed of distinct fibre types, which can be defined by the myosin heavy-chain isotypes (MyHC) they express: MyHC-I in the slow oxidative fibres, MyHC-IIA in the fast oxidative fibres, MyHC-IIX and MyHC-IIB in the fast glycolytic fibres (43). We investigated whether the distribution and CSA of oxidative/glycolytic muscle fibre subtypes in the TA muscle was modified in A17.1 mice compared with WT mice. We therefore performed a co-immunostaining of the different myosin heavy chains together with a laminin staining to determine the CSA (Fig. 8A). The distribution analysis revealed that the A17.1 muscles had more MyHC-IIA fibres (17 versus 9% in WT muscle) and fewer MyHC-IIB (48 versus 56% in WT muscle) (Fig. 8B). Interestingly, this result is in accordance with the down-regulation of Myl1 mRNA (fast myosin light chain) observed by quantitative PCR (Fig. 3). By plotting the frequency distribution of CSA myofibre subtypes, we observed a shift towards the small size for the specific MyHC-IIB and MyHC-IIX fibres, whereas surprisingly the MyHC-IIA fibres were unaffected (Fig. 8C). This result suggests that the fast glycolytic fibres are specifically affected in the A17.1 mice.

Figure 8.

The myosin heavy chain (MyHC) muscle fibers subtypes were determined by immunostaining. (A) Immunostaining of laminin (green), MyHC-IIA (red), MyHC-IIB (blue) on a TA muscle cryosection. The distribution of muscle fiber subtypes (B) and the frequency of the cross-sectional area (C) of each muscle fiber subtype were determined in the whole of TA muscle from WT and A17.1 mice at 26 weeks. The data represented are the mean of three different muscles for each group. *P < 0.05; and χ2 analysis performed on data sets: MyHC-IIX P < 0.001 and MyHC-IIB P < 0.001.

Figure 8.

The myosin heavy chain (MyHC) muscle fibers subtypes were determined by immunostaining. (A) Immunostaining of laminin (green), MyHC-IIA (red), MyHC-IIB (blue) on a TA muscle cryosection. The distribution of muscle fiber subtypes (B) and the frequency of the cross-sectional area (C) of each muscle fiber subtype were determined in the whole of TA muscle from WT and A17.1 mice at 26 weeks. The data represented are the mean of three different muscles for each group. *P < 0.05; and χ2 analysis performed on data sets: MyHC-IIX P < 0.001 and MyHC-IIB P < 0.001.

In order to further confirm this selective muscle atrophy of the fast glycolytic fibres, we analysed two other muscles: the extensor digitorum longus (EDL) muscle considered as a ‘fast’ muscle type and composed of MyHC-IIA, -IIX and -IIB fibres like the TA, and the soleus (SOL) muscle, considered as a mixed muscle type and composed of MyHC-I and MyHC-IIA fibres. As shown in Figure 9A, the muscle mass of the EDL of A17.1 mice was reduced by 20% when compared with WT mice, whereas the muscle mass of the SOL was unchanged in A17.1 and WT mice at 6, 18 and 26 weeks. This difference between the SOL and the EDL further suggests that muscle atrophy in A17.1 mice is restricted to fast glycolytic fibres. The maximal force measurement of these two muscle types revealed a decrease in force for the EDL of A17.1 mice, whereas for the SOL muscle, we did not observe any difference in the maximal force between WT and A17.1 mice (Fig. 9B), confirming that the SOL muscle is spared. By quantitative PCR we further observed in the EDL muscle a 2-fold increase in MuRF-1 expression similar to previous results observed in quadriceps and TA muscle, whereas we did not observe any statistical difference for MuRF-1 expression in the soleus muscle (Fig. 9C).

Figure 9.

The weight and functional performance of the soleus (SOL) and extensor digitorum longus (EDL) was evaluated in A17.1 and WT mice. (A) The muscle mass of the SOL and EDL muscles of A17.1 and WT mice was measured at 6, 18 and 26 weeks of age (6 weeks n = 4 per group; 18 weeks n = 6 per group; 26 weeks n = 6 per group; ***P < 0.001; NS non significant). (B) The maximal force of SOL and EDL was evaluated for A17.1 and WT mice at 26 weeks (*P < 0.05; NS, non-significant). (C) MuRF1 mRNA expression in EDL and soleus muscle from 6-week-old A17.1 and WT mice. Values measured by quantitative RT–PCR are means ± standard deviations for n = 4–6 mice per group (**P < 0.01; NS non significant).

Figure 9.

The weight and functional performance of the soleus (SOL) and extensor digitorum longus (EDL) was evaluated in A17.1 and WT mice. (A) The muscle mass of the SOL and EDL muscles of A17.1 and WT mice was measured at 6, 18 and 26 weeks of age (6 weeks n = 4 per group; 18 weeks n = 6 per group; 26 weeks n = 6 per group; ***P < 0.001; NS non significant). (B) The maximal force of SOL and EDL was evaluated for A17.1 and WT mice at 26 weeks (*P < 0.05; NS, non-significant). (C) MuRF1 mRNA expression in EDL and soleus muscle from 6-week-old A17.1 and WT mice. Values measured by quantitative RT–PCR are means ± standard deviations for n = 4–6 mice per group (**P < 0.01; NS non significant).

Since we found a selective muscle atrophy of the EDL but not of the SOL muscle, we determined whether such a difference could be due to differential transgene expression levels. The transgene is under the control of the HSA promoter, which restricts the transgene expression to skeletal muscles, including the SOL as well as the EDL (44–46). By quantitative RT–PCR, we confirmed that there were equal mRNA expression levels in both the SOL and EDL muscles (Fig. 10A). Thus differential transgene expression cannot explain the selective muscle involvement. Therefore, we continued by performing a direct comparison by immunohistochemical staining of expPABPN1 expression in EDL and SOL muscle sections of 26-week-old A17.1 and WT mice. The PABPN1 immunostaining revealed a similar pattern of expression in EDL, SOL and TA with a high PABPN1 signal observed in around 45% of the nuclei in all muscle types (Fig. 10B). Interestingly, when we performed a KCl treatment to remove soluble proteins, the amount of aggregates in the SOL was higher than in WT, but still two-fold lower than the levels observed in the TA of A17.1 animals (Fig. 10C). Our results thus demonstrate that muscle atrophy in A17.1 mice is specific to fast glycolytic fibres and that these fibres contain a larger number of KCl-resistant INIs. In slow and fast oxidative fibres that do not show muscle atrophy, fewer INIs are observed.

Figure 10.

expPABPN1 expression in TA, EDL and SOL muscles before and after KCl treatment. (A) Quantitative RT–PCR on EDL and SOL muscle at 6 weeks of age (n = 3 for WT; n = 6 for A17 samples). (B) Immunostaining of expPABPN1 in muscle cryosections (TA, EDL and SOL) without any KCl treatment (expPABPN1 in green and nuclei stained with Hoechst in blue). (C) Amount of expPABPN1 positive nuclei before and after KCl treatment to remove any soluble protein (n = 4 per group with around 250–350 fibers counted per muscle, TA and SOL (**P < 0.01 ANOVA test).

Figure 10.

expPABPN1 expression in TA, EDL and SOL muscles before and after KCl treatment. (A) Quantitative RT–PCR on EDL and SOL muscle at 6 weeks of age (n = 3 for WT; n = 6 for A17 samples). (B) Immunostaining of expPABPN1 in muscle cryosections (TA, EDL and SOL) without any KCl treatment (expPABPN1 in green and nuclei stained with Hoechst in blue). (C) Amount of expPABPN1 positive nuclei before and after KCl treatment to remove any soluble protein (n = 4 per group with around 250–350 fibers counted per muscle, TA and SOL (**P < 0.01 ANOVA test).

DISCUSSION

The aim of the present study was to gain further insight into the biological pathways modified in OPMD muscles by a combination of transcriptomic and physiological studies. To generate a comprehensive picture of the deregulated pathways during disease progression in this mouse model, we have selected three time points for the transcriptomic analysis: 6 weeks as an early time point before onset of disease symptoms and 18 and 26 weeks when the mice show progressive muscle weakness (18,29). We observed a massive gene deregulation in A17.1 mice when compared with WT mice at all time points. Among the GO terms revealed in this study, we identified several pathways deregulated such as mRNA processing, cell cycle, protein transport, mitochondria and apoptosis, which corroborate a previous gene-based transcriptome analysis of an in vitro OPMD cell model (13). We also found a deregulation of genes involved in muscle development and muscle cell differentiation, which could potentially emphasize defects in continuous remodelling of muscle, previously demonstrated in OPMD (25,47,48). By mapping the biological concepts associated with this deregulation, we found that the muscle-restricted expression of expPABPN1 induced major and progressive deregulation of genes associated with muscle atrophy. Skeletal muscle atrophy is characterized by a decrease in muscle mass and consequently reduced contractile force of the muscle. Functional and histological analysis of the skeletal muscle of this mouse model confirmed severe muscular atrophy associated with a reduction in muscle strength. This atrophic phenotype was due specifically to the overexpression of the alanine expanded PABPN1 and not simply to overexpression of PABPN1 as we did not observe a severe muscle atrophy in the A10.1 mice expressing WT PABPN1. In accordance with this result, genes associated with atrophy such as MuRF-1 were not changed in the A10.1 mice (data not shown). The transcriptomic analysis showed homology with previous studies describing the transcriptional changes involved in muscle atrophy (49–52), such as increased expression of atrogenes involved in protein degradation and decreased expression of genes involved in energy production. Two major pathways mediate protein degradation in skeletal muscle: the autophagic/lysosomal pathway and the ubiquitin-proteasomal pathway (UPP). In the A17.1 skeletal muscles, we confirmed at all time point an up-regulation of the muscle-specific ubiquitin ligase MuRF-1 gene expression. Since MuRF-1 is a known atrogene playing a crucial role in the loss of muscle proteins (38,42,53–55), these data together with the increased proteasome activity in A17.1 muscles suggest an increased protein degradation rate in A17.1 mice related to muscle atrophy. These data also further support previous studies, which showed that the proteasome is thought to be the major degradation pathway for PABPN1 (11,17). Interestingly, MuRF-1 has also been described to be a potential energy homeostasis regulator for muscle (56). Together with the deregulation of genes involved in protein degradation, we also observed a deregulation of genes involved in energy production—as described in other atrophic conditions (49–52)—among which a significant cluster of genes related to mitochondrial organization. We observed a decreased mitochondrial respiratory chain complex I activity in skeletal muscle of the A17.1 mice. This suggests some impairment of oxidative phosphorylation that may contribute to the muscle dysfunction observed in this mouse model of OMPD. This is of particular interest since mitochondrial abnormalities have frequently been observed in OPMD patients (57–59). This decrease may solely be the result of a deregulation of genes encoding several subunits of complex I, as observed both in our transcriptomic data and in the previous transcriptomic analysis performed in an OPMD cell culture model (13). Respiratory chain enzymes are also susceptible to free radical-induced oxidative damage (60), therefore an increased oxidative stress may also contribute to the decreased complex I activity, as suggested in the transcriptomic analysis (response to oxidative stress, GO:0006979). Toriumi et al. (61) have recently shown that the polyalanine tract may induce mitochondrial dysfunction with the rupture of the mitochondrial membrane, release of cytochrome c and apoptosis (62). We demonstrated here that the reduction in muscle force was not just a consequence of muscle atrophy—as observed with the reduced specific force—so expPABPN1 expression clearly has a deleterious effect in force production potentially via mitochondrial dysfunction or oxidative stress, which will both need to be studied in more detail.

Interestingly, the detailed characterization of the skeletal muscle phenotype of these mice revealed a selective atrophy of the fast glycolytic fibres that contained the highest number of INIs, whereas the oxidative fibres containing less INIs were spared. This result suggests a fibre-type specificity for both muscular atrophy and INIs formation in OPMD, indicating that depending on the muscle metabolic properties, the expression of expPABPN1 leads to different phenotypes. This raises two questions: why are there more INIs in fast glycolytic fibres? And why are oxidative fibres not affected even if these fibres contain INIs? The presence of INIs in both affected (EDL) and non-affected muscles (SOL) further emphasize the complex and poorly understood role of INIs in OPMD, which is still currently under debate. Whereas several studies have suggested a pathological function of INIs, several other studies have suggested that the INIs might just be the result of a cellular defence mechanism and not the direct cause of the disease. In this OPMD mouse model, we observed before KCl treatment similar amount of expPABPN1 expression in affected and unaffected muscles, which suggests that the soluble form of the protein in oxidative fibres is not toxic. We also observed that fast glycolytic fibres contained progressively larger numbers of INIs and were progressively atrophied, which could support the pathological function of aggregates. However, the presence of unaffected oxidative fibres containing INIs suggest that INIs are not the only factor involve in muscle atrophy. The difference in the amount of INIs will need to be more extensively studied to understand why more aggregates are found in fast glycolytic fibres when compared with slow oxidative fibres. There might be a fibre-type-specific mRNA/protein preventing (in oxidative fibres) or enhancing (in glycolytic fibres) the formation of INIs, or these two muscle fibre types may have a different protein degradation system. These two hypotheses need to be evaluated in the future. We also have to keep in mind that oxidative fibres seems to be more resistant to atrophy through a protective mechanism mediated by enhanced antioxidant gene expression (38,63,64), and therefore might be more resistant to the presence of expanded PABPN1. Another possible mechanism for this selective atrophy is based on the fact that nuclei in slow fibres contain a smaller myonuclear domain than fast fibres (65,66); so nuclear defects could potentially have fewer consequences and be less visible in slow fibres.

To summarize, we have shown that expression of expPABPN1 in muscle fibres leads to a massive gene deregulation with muscle atrophy as a major consequence. The muscle weakness we have observed results both from a reduction in muscle mass and a muscle dysfunction due to increased fibrosis, mitochondrial defects and possible oxidative stress. At the fibre-type level, we showed that only glycolytic fibres containing the largest number of INIs were affected, whereas oxidative fibres were spared and contained less INIs. In conclusion, expression of mutant PABPN1 in skeletal muscle of the A17.1 mouse recapitulates several pathological observations seen in OPMD patients: progressive muscle weakness, muscle atrophy, fibrosis, mitochondrial defects, affected and unaffected muscle containing INIs. These molecular and pathological changes will improve our understating of the disease progress in OPMD patients and should provide targets for future therapeutic strategies that may reverse some or all of these modified pathways essential for muscle homeostasis and normal function.

MATERIALS AND METHODS

Mice

A17.1 transgenic mice have previously been described (18). Male A17.1 mice and WT controls were generated by crossing the heterozygous carrier strain A17.1 obtained from Rubinsztein's group (18) with the FvB background mice. The mice were genotyped by PCR 3–4 weeks after birth. Wild type FvB and A17.1 mice were housed in minimal disease facilities (Royal Holloway, University of London) with food and water ad libitum.

RNA isolation and microarray processing

Total RNA was extracted from skeletal muscles using RNA Bee (Amsbio) according to the manufacturer's instructions. RNA integration number (RIN) was determined with RNA 6000 Nano (Agilent Technologies). RNA with RIN >7 were used for subsequent steps. RNA labelling was performed with the Illumina® TotalPrep RNA Amplification kit (Ambion) according to the manufacturer's protocol, and subsequently was hybridized to Illumina Mouse v1.1 Bead arrays.

Data processing and analysis

Before data analysis, microarray measurements were normalized to remove systematic errors by balancing the fluorescence intensities using the quantile method (67). Each time point has been normalized separately. Next, PCA plots were generated to assess the quality of the data (30,31). This analysis showed that 47% of the variations within each data set were attributed to the genetic variation between the WT and the transgenic mice. Subsequently, statistical analysis was conducted using limma package in R (37) to identify genes with significant differences in expression pattern between A17.1 and WT. Statistical analysis includes a cut-off P-value of 0.05 and FDR correction provided in the limma package in R. Probe annotation was made with the Illumina mouse whole-genome bead array version 1 annotation package.

GO analysis

The illuminaMousev1BeadID was used to describe the gene clustering arrangements based on the vocabulary of GO. These clusters have been used to conduct the significance of GO terms using global test (32) by assigning a P-value to each cluster based on the assessment of how well group labels can be predicted for different samples (A17.1 versus WT) based on a regression model. The significance of these GO terms was validated using enrichment analysis. Enrichment analysis uses a hypergeometric test to calculate the significance of each cluster based on the number of differentially expressed genes it holds. In this study, we preferred global test for assessing the significance of GO terms over enrichment method due to an unrealistic assumption in which genes are treated as black and white (differentially or non-differentially expressed) for conducting the significance of each GO category whereas, in global test, gene expression profiles are being used to conduct such an analysis. Subsequently, DAVID functional annotation clustering tool (33,34) has been applied to remove redundancy and increase the specificity threshold for selected pathways, and finally, the list of deregulated genes was mapped to the concepts in biomedical literature using Anni 2.0 (36). GO categories were selected based on the combination of the following criteria (1): GO categories with the adjusted P-value of <0.05; (2) clusters of GO categories generated by DAVID, which have P-values >0.05 will be discarded from the analysis; (3) GO categories that contain at least five genes and less than 1000; (4) from each cluster of GO categories, generated by DAVID, only two were selected for follow-up studies to reduce the redundancy. Subsequently, the 2336 genes that were differentially expressed throughout all three time points were mapped to biomedical concepts using Anni 2.0.

Definition of muscle atrophy-related genes

Muscle atrophy-related genes are defined as differentially expressed genes associated with the term ‘muscle atrophy’ in the biomedical literature, as determined with the literature analysis tool Anni 2.0 with the association score larger than 0.005.

Real-time RT–PCR analysis

Primers for validation were selected from the gene sequence that harbours the Illumina probe location using Primer 3 plus program. RNA was extracted using RNA Bee (Amsbio) and treated with RQ1 RNase-Free DNase (Promega). Subsequently, RNA was reverse transcribed using RevertAid H Minus M-MuLV First Strand kit (Fermentas) according to the manufacturer's instructions. An amount of 3.6 ng of cDNA was used for quantitative PCR using SYBR green mix buffer (BioRad) in a total of 15 ml reaction volume. PCR was carried out as follows: 4 min at 95°C followed by 40 cycles at 95°C for 10 s and 60°C for 45 s, the program ended in 1 min at 95°C and 1 min at 60°C. Specificity of the PCR product was checked by melting-curve analysis using the following program: 65°C increasing 0.5°C in 60 steps of 10s duration. Expression levels were calculated according to the ΔΔCt method normalized to the mHPRT mRNA expression and to the average of the gene expression level in the WT mice. The statistical significance was determined with Student's t-test.

Measurement of muscle contractile properties

Contractile properties of TA muscle were evaluated by measuring the in situ isometric muscle contraction in response to nerve stimulation as described previously (68). Mice were anaesthetized using a pentobarbital solution (i.p. 60 mg/kg). The knee and foot were fixed with clamps and the distal tendons of the muscles were attached to an isometric transducer (Harvard Bioscience) using a silk ligature. The sciatic nerves were proximally crushed and distally stimulated by a bipolar silver electrode using supramaximal square-wave pulses of 0.1 ms duration. All data provided by the isometric transducer were recorded and analysed using PowerLab system (4SP, AD Instruments). All isometric measurements were made at an initial length L0 (length at which maximal tension was obtained during the twitch). Responses to tetanic stimulation (pulse frequency from 6.25, 12.5, 25, 50, 100 and 143 Hz) were successively recorded and the maximal force was determined. After contractile measurements, mice were sacrificed with an overdose of anaesthetic solution. Muscles were then weighed to calculate the specific maximal force, frozen in isopentane cooled in liquid nitrogen and stored at −80°C.

The isometric contractile properties of soleus and extensor digitorum longus muscles were studied in vitro. Measurements were performed as described previously (69). The muscles were dissected free from adjacent connective tissue and soaked in an oxygenated Tyrode solution (95% O2 and 5% CO2) containing (mM): NaCl (118), NaHCO3 (25), KCl (5), KH2PO4 (1), CaCl2 (2.5), MgSO4 (1), glucose (5), and maintained at a temperature of 20°C. Muscles were connected at one end to a force transducer. After equilibration (30 min), electrical stimulation was delivered through electrodes running parallel to the muscle. Isometric contractions were recorded at the length at which maximal isometric tetanic force was observed (L0). Absolute maximal isometric force (mN) was measured (usual frequency of 125 Hz, train of stimulation of 1500 ms). Specific maximal force (mN/mm2) was calculated by dividing the force by the estimated CSA of the muscle. Assuming that muscles have a cylindrical shape and a density of 1.06 mg mm−3, muscle CSA corresponds to the wet weight of the muscle divided by its fibre length (Lf). The fibre length to L0 ratio of 0.70 (soleus) or 0.45 (EDL) was used to calculate Lf. Muscles were weighed and frozen in liquid nitrogen.

Muscle histology, immunohistochemistry and morphometric measurements

Recovered tissues were mounted in Cryo-M-Bed (Bright Instruments, Huntingdon, UK) and snap frozen in liquid nitrogen-cooled isopentane. Staining was carried out on transverse serial cryosections of muscles (10 µm). The muscles were sectioned at 10–12 different intervals along the length of the muscle, allowing the maximal CSA to be determined. For the assessment of tissue morphology and visualization of fibrosis and connective tissue, transverse sections of muscles were stained, respectively, with H&E and Sirius red for further examination under a light microscope. To assess central nucleation, three random areas were assessed in each section. The total number of fibres in these areas was counted and the number of centrally nucleated fibres was expressed as a percentage of the total number of fibres. For morphometric and fibre-type analyses, sections were air-dried, washed in phosphate-buffered saline (PBS) with 0.1% (v/v) Tween-20 (PBS-T) and stained for laminin (Dako, Z0097, Dako, Trappes, France) or for the different MyHC isoforms, with antibodies harvested from hybridoma cell lines obtained from the American Type Culture Collection (Manassas, VA, USA): BA-D5 (IgG2b, anti-MHCI), SC-71 (IgG1, anti-MHCIIa), BF-F3 (IgM, anti-MHCIIb) and 6H1 (IgM, anti-MHCIIX). The sections were incubated at room temperature for 1 h in a blocking solution [bovine serum albumin (BSA) 1%, sheep serum 1%, triton X-100 0.1%, sodium azid 0.001%]. Sections were then incubated at room temperature for 2 h with anti-MyHC-I (BA-D5, 2:3) and anti-MyHC-IIA (SC-71, 1:3). Sections were then incubated overnight at 4°C with anti-laminin (1:300) and anti-MyHC-IIb (BF-F3, 1:1) or anti-MyHC-IIX (6H1, 1:1). Sections were washed as before and secondary antibodies were applied for 1 h at a dilution of 1:400. Alexa 350 anti-mouse IgG2b, Cy3 anti-mouse IgG1, Alexa 647 anti-mouse IgM and Alexa 488 goat anti-rabbit were obtained from Vector Laboratories, Inc. (Burlingame, CA, USA). Metamorph software (Roper Scientific) was used to analyse the number, CSA and MyHC isoforms of fibres. For each muscle, the entire section was analysed.

For PABPN1 immunodetection, sections were blocked with 1% normal goat serum in 0.1 m PBS, 0,1% Triton X100 and incubated overnight at 4°C in primary antibody (a gift from Prof. Elmar Whale, Halle Germany) diluted to 1:500 in the same buffer. Slides were washed, incubated for 1 h with an anti-dystrophin antibody for fibre detection (NCL-Dys1 mouse monoclonal IgG2a, Novocastra), further incubated with respective secondary antibodies for 2 h at room temperature and stained with Hoechst to visualize nuclei. When necessary, sections were incubated in 1 m KCl, 30 mm HEPES, 65 mm PIPES, 10 mm EDTA, 2 mm MgCl2, pH 6.9, for 1 h prior to the immunolabelling, to remove any soluble proteins.

Images were visualized using an Olympus BX60 microscope (Olympus Optical, Hamburg, Germany), digitalized using a CCD camera (Photometrics CoolSNAP fx; Roper Scientific, Tucson, AZ, USA) and analysed using MetaView image analysis system (Universal Imaging, Downington, PA, USA).

Proteasome peptidase activities

After dissection, TA from A17.1 and WT mice were homogenized for cytosolic extraction in a Polytron homogenizer (low setting, 3 s) using an ice-cold buffer containing: 50 mm Tris–HCl (pH 7.5), 250 mm sucrose, 5 mm MgCl2, 2 mm ATP, 1 mm DTT, 0.5 mm EDTA and 0.025% digitonin, as reported previously (70). The homogenate was centrifuged at 20 000g for 15 min at 4°C. The pellet was discarded and the supernatant represents the cytosolic fraction (70). Protein quantification was made using the Bradford method (Pierce), with BSA as a standard. Peptidase activities of the proteasome were evaluated using appropriate fluorogenic substrates as described previously (71). Chymotrypsin-like (CT-like), trypsin-like (Tryp-like) and caspase-like (Casp-like) activities of the proteasome were assayed using the fluorogenic peptides LLVY-MCA (25 µm), RLR-MCA (40 µm) and LLE-NA (100 µm), respectively (70). The assay buffer was composed of 50 mm Tris–HCl (pH 7.5), 40 mm KCl, 5 mm MgCl2, 1 mm DTT containing the appropriated peptide substrate. Enzymatic kinetics were carried out for 30 min at 37°C using 40 µg of cytosolic protein fractions in a temperature-controlled microplate fluorimetric reader (Fluostar Galaxy, bMG, Stuttgart, Germany). The excitation/emission wavelengths were 350/440 and 333/410 nm for aminomethylcoumarin and beta-naphthylamine products. The rate of proteolysis was determined for each substrate as the mean slope by comparing the linear response of fluorescence with time. Reactions were performed in the presence (20 µm) and absence of the specific proteasome inhibitor N-Cbz-Leu-Leu-leucinal (MG132), to test the specificity of the activity measured.

Mitochondrial enzyme activity

All activities were determined at 30°C. Prior to analysis, cells were subjected to three cycles of freezing and thawing to lyse membranes. Enzyme activities were assessed using an Uvikon 940 spectrophotometer (Kontron Instruments Ltd, Watford, UK). Complex I activity was measured according to the method of Ragan et al. (72). Complex II–III activity was measured according to the method of King et al. (73). Complex IV activity was measured according to the method of Wharton and Tzagoloff (74). Citrate synthase (CS) activity was determined by the method of Shepherd and Garland (75). Enzyme activities are expressed as a ratio to CS (mitochondrial marker enzyme) to compensate for mitochondrial enrichment in the cell samples.

Western blotting

Muscle lysates were prepared by homogenizing tissue in RIPA solution (NaCl 0.15 m; HEPES 0.05 m; NP-40 1%; sodium dehoxycholate 0.5%; SDS 0.10%; EDTA 0.01 m) with protease inhibitor cocktail (Complete, Roche Diagnostics). Proteins were separated on 4–12% Bis–Tris gel (Invitrogen) and transferred onto a nitrocellulose membrane (Hybond ECL membrane; Amersham Biosciences), which was blocked by incubation in 5% milk in 0.1 m PBS, 0.1% Tween-20. Membrane was probed with primary antibodies raised against PABPN1 (gift from Pr. Elmar Wahle, Halle, Germany, 1:2000) or against GAPDH (Santa Cruz, 1:2000) as a loading control. The membrane was further incubated with HRP-conjugated antibodies (Jackson ImmunoResearch; 1:40 000). Immunoreactive bands were detected with enhanced chemiluminescence reagent (ECL; Amersham Biosciences) and signals visualized by exposing the membrane to ECl Hyperfilm (Amersham Biosciences).

Statistical analysis

All data are presented as mean values ± standard error of the mean (SEM) (cohort size stated per experiment). All statistical analyses were performed using either the Student t-test, the ANOVA one-way analysis of variance followed by the Newman–Keuls post-test, or χ2 analysis using GraphPad Prism (version 4.0b; GraphPad Software, San Diego CA, USA). A difference was considered to be significant at *P < 0.05, **P < 0.01 or ***P < 0.001.

SUPPLEMENTARY MATERIAL

Supplementary Material is available at HMG online.

FUNDING

This study was supported by grants from the European Commission ref. LSHM-CT-2005018675 – PolyALA, the Clinigene EC Network of Excellence (LSHB-CT-2006-018933), MYOAGE and MYORES Network of Excellence (contract FP7-LSHG-2007-B-223576 and FP6-511978), Wellcome Trust Senior Clinical fellowship, the Centre for Medical Systems Biology and the Muscular Dystrophy Association (68016), AFM (Association Française contre les Myopathies), INSERM, CNRS and Université Pierre et Marie Curie.

ACKNOWLEDGEMENTS

We are grateful to Prof. Elmar Wahle (University of Halle, Germany) for providing the PABPN1 antibody. We thank Claire Phillips and Nicola Sanderson for assistance with the breeding of the FvB and A17.1 mice, Linda Popplewell for the genotyping, Helen Foster and Alberto Malerba for sample collection, Anne Bigot and Christel Gentil for assistance with western blots. We also thank Julie Dumonceaux, Denis Furling, Arnaud Klein and the MSG for helpful discussions. Finally, we thank Bertrand Friguet for insightful comments and advice related to proteasome activity measurements.

Conflict of Interest statement. None declared.

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Supplementary data