Duchenne muscular dystrophy (DMD) is a debilitating and lethal neuromuscular disorder caused by mutations in the DMD gene located on the X chromosome (Hoffman et al., 1988). Diagnosis is made between the age of 2 and 5, loss of ambulation occurs around 12 and young adults die due to respiratory and/or cardiac failure (Koeks et al., 2017).

DMD is the largest human gene known (Tennyson et al., 1995). Three full-length transcripts encode 427 kDa proteins, while further intragenic promoters drive the expression of progressively truncated variants. Dp427 and the dystrophin-associated protein complex (DAPC) are important for the functional development of differentiating myotubes (Shoji et al., 2015) and subsequently prevent contraction-induced injury in the mature muscle (Rader et al., 2016).

Several studies showed that the ablation of dystrophin in fully differentiated myofibres did not trigger their degeneration (Rader et al., 2016; Ghahramani Seno et al., 2008), and even that myofibres can function without dystrophin (Waugh et al., 2014; Vieira et al., 2015).

In fact, DMD pathology is active prior to diagnosis: delays in the attainment of motor and non‐motor milestones are discernible in 2 months old DMD babies (van Dommelen et al., 2020) and transcriptomes of muscles from asymptomatic DMD patients revealed typical dystrophic abnormalities (Pescatori et al., 2007). Studies of human foetuses (Toop and Emery, 1977; Emery, 1977; Vassilopoulos and Emery, 1977) and various animal DMD models, including GRMD dogs (Nguyen et al., 2002), Sapje zebrafish (Bassett et al., 1977) and Dmd^mdx mouse embryos (Merrick et al., 2009) revealed that the pathology starts already in prenatal development.

Indeed, in skeletal muscle lineages modelled in human DMD pluripotent stem cells, we have recently demonstrated marked transcriptome and miRNA dysregulations identifiable even before muscle specialisation (Mournetas et al., 2021). These data, combined with the existence of the specific embryonic dystrophin Dp412e (Massouridès et al., 2015), substantiate the early disease manifestations in muscle precursor cells.

Importantly, there is increasing evidence that dysregulation of myogenic cells is behind muscle pathology and disease progression in adult DMD muscle. However, the mechanism(s) remain(s) to be elucidated and, given the interaction of myogenic and inflammatory cells in muscle regeneration (Tidball, 2017), distinguishing the primary and the secondary consequences of dystrophin deficiency is crucial for the development of effective therapies.

Helen Blau proposed that DMD is intrinsic to the undifferentiated myoblast (Blau et al., 1983). This hypothesis was initially discounted (Hurko et al., 1987), but there is new evidence that DMD mutations produce a range of cell-autonomous abnormalities in both human and mouse myogenic cells from adult muscles (Yablonka-Reuveni and Anderson, 2006; Yeung et al., 2006; Sacco et al., 2010; Górecki, 2016).

If DMD directly affects myoblasts, cells that are key to muscle regeneration, a better definition of the consequences of the loss of DMD gene expression could help identify early disease biomarkers and establish better therapeutic targets.

Herein, using a combination of RNA-Seq, molecular and functional approaches, we compared dystrophic and healthy myoblasts isolated from skeletal muscles of the commonly used Dmd^mdx mouse model. The Dmd^mdx has a stop codon mutation in exon 23 (Sicinski et al., 1989) resulting in the loss of full-length (Dp427) dystrophin expression, which reflects the molecular defect affecting the majority of DMD patients. Detailed histological and molecular analyses indicated that the clinical phenotype in 3–10 weeks old Dmd^mdx is as aggressive as in Duchenne patients (Duddy et al., 2015; DiMario et al., 1991; Massopust et al., 2020). Therefore, during this period, Dmd^mdx is a good model for the human pathology. Unlike human, after this phase of severe degeneration-regeneration, most Dmd^mdx muscles start to regenerate (DiMario et al., 1991) due to an unknown mechanism.

While Dp427 is expressed in satellite cells and myofibres and its loss in Dmd^mdx reflects the predominant molecular defect in DMD patients, dystrophin Dp71 is present in regenerating muscles and specifically in myoblasts (Howard et al., 1999). Yet, little attention has been given to the DMD pathology in the dystrophin-null individuals. Given that we have recently described exacerbated pathology in Dmd^mdx-βgeo mice lacking all dystrophins (Young et al., 2020), we also investigated the consequences of the total loss of Dmd expression in Dmd^mdx-βgeo myoblasts (Wertz and Füchtbauer, 1998).

Finally, we compared transcriptomic alterations in mouse and human dystrophic myoblasts to identify defects occurring across species. Given that skeletal muscle is a key metabolic site, accounting for around 90% of total oxygen uptake, muscle metabolism is a significant variable (Zurlo et al., 1990). Moreover, metabolic dysfunction can impair muscle regeneration, and the impact of DMD on cellular energetics in muscle progenitors has recently been identified (Matre et al., 2019). Therefore, using the RNA-Seq profiles, we reconstructed the human myoblast-specific enzyme-constrained genome-scale metabolic model.

We report that the absence of expression of the full-length dystrophin triggers major transcriptomic and functional abnormalities in myoblasts. These abnormalities are cell autonomous, as they persist in myogenic cells maintained long-term in culture. Importantly, key alterations are common between mouse and human myoblasts.

DMD presents in muscle stem cells, where the loss of dystrophin affects asymmetrical cell divisions (Dumont et al., 2015b). Subsequently, the absence of dystrophin during myotube differentiation causes typical dystrophic abnormalities such as altered calcium homeostasis and creatine kinase leakage (Shoji et al., 2015). We hypothesised that myoblasts originating from dystrophic stem cells, which give rise to myotubes, are also affected. Indeed, we demonstrate here for the first time that, in proliferating human and mouse myoblasts, the absence of DMD gene expression results in major abnormalities. Our data combining global RNA-Seq and functional analyses demonstrate that DMD directly affects myoblasts.

The key alterations concerned proliferation, chemotaxis, and differentiation. Importantly, synchronisation of these three processes, involving exit from the cell cycle, migration to the site of damage, activation of the skeletal muscle-specific genes and cell fusion, is essential for muscle development and regeneration. Ultimately, myoblasts are the effector cells of muscle growth and repair, and it is the failure of muscle regeneration that drives DMD progression.

These defects are cell autonomous rather than caused by the inflammatory environment of the dystrophic niche because they persist in the dystrophic cell line maintained long-term in culture: established cells reproduced the key transcriptomic anomalies and were described before to have functional alterations found in primary cells (Róg et al., 2019).

Moreover, alterations are shared between mouse and human DMD myoblasts, despite the significant heterogeneity within human samples (Mournetas et al., 2021; Choi et al., 2016). Therefore, these dystrophic anomalies manifest irrespective of genetic backgrounds and across species, which confirms their significance. Furthermore, altered proliferation, migration and differentiation in DMD myoblasts have been reported (Witkowski and Dubowitz, 1985; Melone et al., 2000; Nesmith et al., 2016; Sun et al., 2020) and our data provide the molecular underpinning for these abnormalities.

DMD pathology being intrinsic to the myoblast has been proposed previously (Blau et al., 1983) and there has been growing evidence that DMD mutations cause a range of cell-autonomous abnormalities in human and mouse myogenic cells and myoblasts, which affect cell division, differentiation, energy metabolism and signalling (Yablonka-Reuveni and Anderson, 2006; Yeung et al., 2006; Sacco et al., 2010; Sacco et al., 2010; Young et al., 2020; Young et al., 2020; Dumont et al., 2015b; Onopiuk et al., 2009; Onopiuk et al., 2015; Dumont et al., 2015a; Young et al., 2015). Interestingly, Dmd^mdx cell migration found reduced under physiological stimuli (this work) can be increased in the inflammatory environment (Young et al., 2020). In fact, altered proliferation and migration resulting in highly metastatic phenotypes, have been associated with the loss of Dp427 in tumours featuring myogenic differentiation (Wang et al., 2014).

Our team recently demonstrated that DMD mutations evoke marked transcriptome and miRNome dysregulations early in human muscle cell development (Mournetas et al., 2021). In that study, expression of key coordinators of muscle differentiation was dysregulated in proliferating dystrophic myoblasts, the differentiation of which was subsequently also found to be altered, in line with the mouse cells studied here (Figure 5c).

A significant number of downregulated transcripts in dystrophic myoblasts are controlled by the same TFs, most notably MyoD and MYOG. Given that MYOG itself is regulated by MyoD, the significant downregulation of Myod1 expression found here appears to be the key trigger mechanism of the dystrophic abnormality in myoblasts: the lower levels of Myod1 transcript strongly correlate with downregulated expression of genes controlled by this TF, and which are key coordinators of skeletal muscle repair. Consequently, altered expression of myogenic regulatory factors in dystrophic myoblasts could have multiple consequences. Indeed, complete Myod1 ablation in Dmd^mdx has been shown to exacerbate the disease due to a diminished capability for muscle regeneration (Megeney et al., 1999; Schuierer et al., 2005). Downregulation of MyoD, which inhibits the cell cycle, could explain the increased proliferation in dystrophic cells. The Myod1-Mymk-Mymx (Myomaker/ Myomixer) axis altered here was found to determine myotube formation (Zhang et al., 2020). Given that Pax3 inhibits myogenic differentiation of myoblasts (Epstein et al., 1995), its downregulation in dystrophic cells could also contribute to this accelerated differentiation pattern observed here.

GO categories concerning calcium homeostasis and signalling, known to be altered in dystrophic muscles across species, were also found altered in dystrophic myoblasts: calsequestrins 1 and 2 levels being significantly downregulated, in line with previous findings in the dystrophic muscle Waugh et al., 2014; Doran et al., 2004 and cell lines (Róg et al., 2019; Onopiuk et al., 2015).

Given that Dp71 dystrophin is found in undifferentiated myogenic cells (Howard et al., 1999), and that in development shorter dystrophins have been associated with proliferation and migration, and long isoforms with terminal commitment (Hildyard et al., 2020), we hypothesized that eliminating expression of Dp71 may exacerbate dysfunctions in myogenic cells. Surprisingly, we found that both Dmd^mdx and Dmd^mdx-βgeo cells had similar abnormalities and, contrary to Dp71 overexpression increasing myoblast proliferation (Farea et al., 2020), BrdU incorporation in dystrophin-null myoblasts was still significantly increased compared to WT. Interestingly, Dmd^mdx myoblasts, having a point mutation in exon 23, expressed significantly lower levels of Dp71 (Appendix 1—figure 1), whose expression is driven by a promoted located over 40 exons downstream of the mutation. But whether this downregulation is sufficient to mimic its total loss is unknown.

Combined, these data bring to light the significance of full-length dystrophin defects in undifferentiated muscle cells, the consequences of which correspond to the impaired muscle regeneration occurring in this disease.

Clearly, the important question is the mechanism altering the myoblast phenotype. Dp427 in satellite cells appears to control asymmetrical cell division (Dumont et al., 2015b), which might suggest an interaction problem also in myoblasts.

However, even healthy myoblasts do not express Dp427 protein at levels detectable by Western blotting: an observation that contributed to the belief that myoblasts are not affected by DMD mutations. Although our attempts at immunolocalization in unsynchronised primary myoblasts failed to detect Dp427 (Appendix 1—figure 2), a precise spatio-temporal requirement for small amounts of full-length dystrophin, analogous to its role in satellite cells Dumont et al., 2015b or at neuronal synapses Lidov et al., 1990 is a possibility that should be studied. Importantly, WT myoblasts express the full-length transcript, which is depleted in dystrophic cells. It is worth noting that this 14 kb mRNA, which transcription time may be as long as 16 hr Tennyson et al., 1995, is produced in rapidly dividing myoblasts. Its expression is therefore unlikely to be meaningless. Indeed, downregulation of full-length dystrophin occurs across various malignancies and is associated with alterations of molecular pathways that were identical with alterations found in Duchenne muscles. Moreover, low DMD gene expression in tumours was associated with poor patient survival (https://www.biorxiv.org/content/10.1101/2022.04.04.486990v3). Clealry, dystrophin mis-expression is far more consequenctial and can affect many more cells and tissues than previously understood.

Is it possible that loss of the full-length dystrophin expression may trigger a mechanism not involving its protein product? Considering the increasing number of human pathologies caused by RNA, including another dystrophy – dystrophia myotonica Kino et al., 2018, it is not inconceivable that loss of a large 14 kb transcript made up of 79 exons may trigger a novel RNA-mediated disease process Chan et al., 2018. Abnormalities in Dmd^mdx cells, that harbour just a point mutation, indicate that the patho-mechanism does not require large Dmd gene rearrangements Sudmant et al., 2015. Yet, improvements resulting from the expression of mini/micro dystrophins do not support the requirement for the entire 14 kb transcript or a toxicity of mRNA breakdown products generated via nonsense-mediated decay. However, the impact of premature dystrophin transcript termination on the downregulation of other genes Kamieniarz-Gdula and Proudfoot, 2019 cannot be excluded.

A more likely explanation involves alterations of epigenetic regulation, a mechanism of increasing importance in DMD: The absence of dystrophin/DAPC in satellite cells leads to aberrant epigenetic activation, which impairs functions of the newly generated cells. This dystrophic SC division defect has been linked to polarization problem due to MARK2 mis-interaction Chang et al., 2016 combined with β-syntrophin abnormalities Dumont and Rudnicki, 2016; Ribeiro et al., 2019. Downregulation of β-syntrophin, a DAPC member, results in impaired polarization of its interactor - p38γ. This, in turn, increases CARM1 phosphorylation and reduces MYF5 activation by PAX7 in in the daughter cell due for myogenic differentiation Chang et al., 2018. Furthermore, dystrophin anchors nNOS Brenman et al., 1995, which becomes mis-localised in dystrophic cells. This results in decreased nitric oxide-dependent S-nitrosylation of HDAC2 Colussi et al., 2008 and higher deacetylase activity that has been shown to be involved in the progression of muscular dystrophy Minetti et al., 2006.

Thus, loss of the full-length dystrophin in muscle stem cells does not abrogate myogenic cell divisions but causes significant abnormalities. The descendant myoblasts appear to be harbouring somatically heritable epigenetic changes analogous to genomic imprinting Tucci et al., 2019 and themselves might manifest significant epigenotype abnormalities Zoghbi et al., 2016.

Indeed, our DMD knockdown and rescue experiments in skeletal muscle progenitor cells Mournetas et al., 2021 showed that phenotypic changes at day 17 were the consequences of some earlier events that occurred at day 10 and that some abnormalities might not be reproduced nor alleviated after crossing a specific checkpoint.

The data here indicate that epigenetic changes in dystrophic primary myoblasts may be present, as amongst the significantly altered genes Hist2h2ac, Hist1h2ag, Hist1h2ah, Hist1h1a, Smyd1 and Hmga1 gene products are involved in chromatin modifications or belong to the GO category ‘negative regulation of gene expression, epigenetic’. Methylated histone H3 lysine 4 (H3K4) is a key epigenetic signal for gene transcription. H3K4 methylation is mediated by several methyltransferases, including muscle-active Smyd1 Tan et al., 2006, found among the top downregulated transcripts in dystrophic myoblasts. Histone methylation can be reversed by histone demethylases, of which Lysine-Specific Histone Demethylase 1 A (LSD1 encoded by Kdm1a) is an important muscle enzyme Tosic et al., 2018. The network of interactions between genes in this aforementioned GO category (Appendix 1—figure 3, Supplementary file 1-8) showed a clear interface between the Kdm1a node and significantly downregulated histone genes. LSD1 is involved in controlling Myod1 expression Scionti et al., 2017, which we found significantly downregulated in dystrophic myoblasts. Importantly, treatment of Dmd^mdx mice with histone deacetylase inhibitors (HDACi) promoted myogenesis Saccone et al., 2014. Epigenetically, HDACi upregulate Myod1 Mal et al., 2001 and therefore would counteract its decreased expression and the resulting downregulated expressions of genes controlled by this TF, which we described here.

We postulate that, in dystrophic myoblasts, epigenetic dysregulation of Myod1 expression causes a pathological cascade of downregulated transcriptions of a range of genes controlled by MyoD, with functional consequences for muscle regeneration.

If it is the absence of dystrophin in myogenic cells that determines their fate, developmental muscle abnormalities should occur. This is indeed the case: asymptomatic DMD patients already have transcriptomic alterations Pescatori et al., 2007 and myogenesis in Dmd^mdx embryos is severely disrupted, with myotube hypotrophy, reduced myotube numbers and displacement defects Merrick et al., 2009. This developmental abnormality continues in adult muscles during regeneration.

Altogether, our data identify a continuum, where mutations disrupting expression of full-length dystrophin cause SC division abnormalities impacting myoblast generation but also imprinting these cells with functionalities further reducing muscle regeneration. And as dystrophin is needed at the myotube stage for the initial assembly of the DAPC and subsequent formation of viable myofibres Shoji et al., 2015, dystrophinopathy results in contraction-induced injury in mature muscle (Rader et al., 2016). Thus, the vicious circle of disease is closed with both degenerative and regenerative abnormalities contributing to DMD progression (Figure 10).

Such a scenario explains the apparent contradiction, where removal of dystrophin and/or DAPC in fully developed myofibres does not result in dystrophic muscle damage (Rader et al., 2016; Ghahramani Seno et al., 2008). It would be the case if, once formed in a healthy muscle, DAPC remains stable even if dystrophin is subsequently lost, as indeed have been demonstrated (Ghahramani Seno et al., 2008).

Moreover, as the efficacy of exon skipping and gene targeting methods is much higher in proliferating cells than in myofibres, all outcomes of these treatments could arise from dystrophin re-expression in myoblasts, which later differentiate into myotubes with the functional DAPC.

Findings described here agree with the existing data that loss of dystrophin expression disrupts many downstream processes. These processes offer good targets for therapeutic interventions that are not constrained by the causative mutation Percival et al., 2012; Vidal et al., 2012; Sinadinos et al., 2015. Importantly, adjustments made in dystrophic myoblasts can reduce or even prevent occurrence of dystrophy. For example, upregulation of Jagged 1 can counteract damage in muscles without any dystrophin Vieira et al., 2015 and corrections to COUP-TFII Xie et al., 2016, EGFR-Aurka and Ghrelin expression Wang et al., 2019 can reduce muscle damage.

Given that these pathways are active in myoblasts and the proteins involved are structurally incompatible with substituting the scaffolding properties of dystrophin, these findings further challenge the role of dystrophin in myofibre sarcolemma stabilization as the key pathological alteration in DMD.

Interestingly, the metabolic analysis indicated a significant alteration in key metabolic pathways in DMD myoblasts, which were in line with alterations in the rate of glycolysis/gluconeogenesis, tricarboxylic acid cycle, fatty acid activation, and propanoate metabolism previously found in DMD patients (as previously reviewed Timpani et al., 2015). Clearly, the DMD impact on metabolism is an early and widespread disease manifestation, with abnormalities presenting already in the embryonic development Mournetas et al., 2021 and present in myoblasts Vallejo-Illarramendi et al., 2014 (and this work), in differentiated muscle and also in dystrophic brains Kreis et al., 2011. As an increase in energy demand is observed in DMD muscle, the upregulation of the glycolysis/gluconeogenesis metabolic pathway suggests the upregulation of glucose metabolism-related enzymes, including hexokinase (D-glucose:ATP) and pyruvate kinase, which compensates for the energy demand Filippelli and Chang, 2021. These changes are also compensated by other metabolic alterations in mitochondrial pathways and reactions, for example the downregulation of mitochondrial beta-oxidation of branched-chain fatty acids, odd-chain fatty acids, and di-unsaturated fatty acids (n-6) Nsiah-Sefaa and McKenzie, 2016. Notably, these metabolic adaptations are activated in dystrophic myoblasts and not just myofibres, which could be easier explained by increased energy demands.

The synthesis of ATP and phosphocreatine is catalysed by creatine kinase in a reversible mechanism Bradley, 2004; Scholte and Busch, 1980. Therefore, creatine kinase, significantly downregulated in Arginine and proline metabolism of mitochondrial centre (Figure 9b) signifies the reduction of ATP production in DMD myoblasts Kelly-Worden and Thomas, 2014. The ATP dysfunction at the mitochondrial level induces several patho-physiological events and should exacerbate dystrophinopathy Scholte and Busch, 1980.

The mitochondrial leukotriene metabolism is found to be significantly elevated in DMD myoblasts, consistently with the fact that muscle can produce leukotrienes, which are implicated in inflammation and contribute to muscle weakening Korotkova and Lundberg, 2014. Furthermore, the observed upregulation in propanoate metabolism leads to the accumulation of propionyl-CoA and associated metabolites in plasma, and increases the levels of propionyl-CoA and protein propionylation, which can impair myogenic differentiation in human myoblasts Lagerwaard et al., 2021.

Glucose-6-phosphate dehydrogenase is known to contribute to the regulation of glucose uptake in skeletal muscle Lee-Young et al., 2016. The downregulated fold change of glucose 6-phosphate dehydrogenase (G6PDH2cNo1) suggests that the concentration of glucose is significantly altered in DMD myoblasts. The reduction in the glucose level could impair the differentiation of skeletal myoblasts through the activation of a pathway that targets the enzymatic activity of SIRT1 Fulco et al., 2008.

Our findings here, combined with the other data, provide compelling foundation for the call to re-evaluate the established belief on the pathogenesis of DMD. Such reconsideration is important because most current therapeutic approaches aim at dystrophin restoration in differentiated myofibres and are often conducted in the advanced-stage disease, with very limited regenerative muscle potential. Despite decades of intensive research and numerous clinical trials, none of the candidate treatments delivered disease-modifying results Rodrigues and Yokota, 2018. Part of the problem is immune responses to re-expressed dystrophin Vila et al., 2019, which need to be tackled Sharma et al., 2017 or prevented. However, dystrophin re-expression using new technologies Mangeot et al., 2019 allowing re-targeting dystrophin to myoblasts and satellite cells in younger patients or, better still, in newborns able to develop neonatal tolerance to dystrophin, could produce effective therapies for this devastating disease.

All data generated or analysed during this study are included in the manuscript and supporting files. Supplementary tables uploaded with the manuscript provide the numerical data that are represented in graphical format in the manuscript figures or as summary tables. RNA-Seq data for the mouse myoblast samples have been deposited in the ArrayExpress database at EMBL-EBI under accession number E-MTAB-10322 and E-MTAB-7287 for primary myoblasts and established cell lines respectively (RNA Sequencing section), and under E-MTAB-8321 for human primary myoblasts (Human primary adult myoblast RNA-Seq data section).

1. 1. Górecki D
   2. Gosselin M
   3. Robson S

   (2022) ArrayExpress

   ID E-MTAB-10322. Loss of full-length dystrophin expression causes major cell-autonomous abnormalities in proliferating myoblasts.

   https://www.ebi.ac.uk/arrayexpress/experiments/E-MTAB-10322

1. 1. Górecki DC
   2. Robson S

   (2019) ArrayExpress

   ID E-MTAB-7287. RNA-seq comparison of immortalised myoblasts from dystrophic and WT mice.

   https://www.ebi.ac.uk/arrayexpress/experiments/E-MTAB-7287
2. 1. Mournetas V

   (2021) ArrayExpress

   ID E‐MTAB‐8321. Human primary adult myoblast RNA-Seq.

   https://www.ebi.ac.uk/arrayexpress/experiments/E-MTAB-8321/

1. 1. Al-Khalidi R
   2. Panicucci C
   3. Cox P
   4. Chira N
   5. Róg J
   6. Young CNJ
   7. McGeehan RE
   8. Ambati K
   9. Ambati J
   10. Zabłocki K
   11. Gazzerro E
   12. Arkle S
   13. Bruno C
   14. Górecki DC

   (2018) Zidovudine ameliorates pathology in the mouse model of duchenne muscular dystrophy via P2RX7 purinoceptor antagonism

   Acta Neuropathologica Communications 6:27.

   https://doi.org/10.1186/s40478-018-0530-4

   • PubMed
   • Google Scholar
2. Software

   1. Andrews S

   (2010) FastQC: a quality control tool for high throughput sequence data, version v3

   Babraham Bioinformatics.

   https://www.bioinformatics.babraham.ac.uk/projects/fastqc/
3. 1. Angione C
   2. Lió P

   (2015) Predictive analytics of environmental adaptability in multi-omic network models

   Scientific Reports 5:1–21.

   https://doi.org/10.1038/srep15147

   • Google Scholar
4. 1. Angione C

   (2018) Integrating splice-isoform expression into genome-scale models characterizes breast cancer metabolism

   Bioinformatics 34:494–501.

   https://doi.org/10.1093/bioinformatics/btx562

   • PubMed
   • Google Scholar
5. 1. Aoyagi T
   2. Wada T
   3. Kojima F
   4. Nagai M
   5. Miyoshino S
   6. Umezawa H

   (1983) Two different modes of enzymatic changes in serum with progression of duchenne muscular dystrophy

   Clinica Chimica Acta; International Journal of Clinical Chemistry 129:165–173.

   https://doi.org/10.1016/0009-8981(83)90212-7

   • PubMed
   • Google Scholar
6. 1. Augusto V
   2. Padovani C
   3. Eduardo G
   4. Campos R

   (2004)

   Skeletal muscle fiber types in C57BL6J mice

   Journal of Morphological Sciences 21:89–94.

   • Google Scholar
7. 1. Bassett DI
   2. Bryson-Richardson RJ
   3. Daggett DF
   4. Gautier P
   5. Keenan DG
   6. Currie PD

   (1977) Dystrophin is required for the formation of stable muscle attachments in the zebrafish embryo

   Development 130:5851–5860.

   https://doi.org/10.1242/dev.00799

   • PubMed
   • Google Scholar
8. 1. Benjamini Y
   2. Hochberg Y

   (1995) Controlling the false discovery rate: A practical and powerful approach to multiple testing

   Journal of the Royal Statistical Society 57:289–300.

   https://doi.org/10.1111/j.2517-6161.1995.tb02031.x

   • Google Scholar
9. 1. Blau HM
   2. Webster C
   3. Pavlath GK

   (1983) Defective myoblasts identified in duchenne muscular dystrophy

   PNAS 80:4856–4860.

   https://doi.org/10.1073/pnas.80.15.4856

   • PubMed
   • Google Scholar
10. Book

    1. Bradley WG

    (2004)

    Neurology in Clinical Practice

    Taylor & Francis.

    • Google Scholar
11. 1. Brenman JE
    2. Chao DS
    3. Xia H
    4. Aldape K
    5. Bredt DS

    (1995) Nitric oxide synthase complexed with dystrophin and absent from skeletal muscle sarcolemma in duchenne muscular dystrophy

    Cell 82:743–752.

    https://doi.org/10.1016/0092-8674(95)90471-9

    • PubMed
    • Google Scholar
12. 1. Capitanio D
    2. Moriggi M
    3. Torretta E
    4. Barbacini P
    5. De Palma S
    6. Viganò A
    7. Lochmüller H
    8. Muntoni F
    9. Ferlini A
    10. Mora M
    11. Gelfi C

    (2020) Comparative proteomic analyses of duchenne muscular dystrophy and becker muscular dystrophy muscles: changes contributing to preserve muscle function in becker muscular dystrophy patients

    Journal of Cachexia, Sarcopenia and Muscle 11:547–563.

    https://doi.org/10.1002/jcsm.12527

    • PubMed
    • Google Scholar
13. 1. Chan LY
    2. Mugler CF
    3. Heinrich S
    4. Vallotton P
    5. Weis K

    (2018) Non-invasive measurement of mrna decay reveals translation initiation as the major determinant of mrna stability

    eLife 7:e32536.

    https://doi.org/10.7554/eLife.32536

    • PubMed
    • Google Scholar
14. 1. Chang NC
    2. Chevalier FP
    3. Rudnicki MA

    (2016) Satellite cells in muscular dystrophy - lost in polarity

    Trends in Molecular Medicine 22:479–496.

    https://doi.org/10.1016/j.molmed.2016.04.002

    • PubMed
    • Google Scholar
15. 1. Chang NC
    2. Sincennes MC
    3. Chevalier FP
    4. Brun CE
    5. Lacaria M
    6. Segalés J
    7. Muñoz-Cánoves P
    8. Ming H
    9. Rudnicki MA

    (2018) The dystrophin glycoprotein complex regulates the epigenetic activation of muscle stem cell commitment

    Cell Stem Cell 22:755–768.

    https://doi.org/10.1016/j.stem.2018.03.022

    • PubMed
    • Google Scholar
16. 1. Chen B
    2. You W
    3. Wang Y
    4. Shan T

    (2020) The regulatory role of myomaker and myomixer-myomerger-minion in muscle development and regeneration

    Cellular and Molecular Life Sciences 77:1551–1569.

    https://doi.org/10.1007/s00018-019-03341-9

    • PubMed
    • Google Scholar
17. 1. Chiron S
    2. Tomczak C
    3. Duperray A
    4. Lainé J
    5. Bonne G
    6. Eder A
    7. Hansen A
    8. Eschenhagen T
    9. Verdier C
    10. Coirault C

    (2012) Complex interactions between human myoblasts and the surrounding 3D fibrin-based matrix

    PLOS ONE 7:e36173.

    https://doi.org/10.1371/journal.pone.0036173

    • PubMed
    • Google Scholar
18. 1. Choi IY
    2. Lim H
    3. Estrellas K
    4. Mula J
    5. Cohen TV
    6. Zhang Y
    7. Donnelly CJ
    8. Richard J-P
    9. Kim YJ
    10. Kim H
    11. Kazuki Y
    12. Oshimura M
    13. Li HL
    14. Hotta A
    15. Rothstein J
    16. Maragakis N
    17. Wagner KR
    18. Lee G

    (2016) Concordant but varied phenotypes among duchenne muscular dystrophy patient-specific myoblasts derived using a human ipsc-based model

    Cell Reports 15:2301–2312.

    https://doi.org/10.1016/j.celrep.2016.05.016

    • PubMed
    • Google Scholar
19. 1. Colussi C
    2. Mozzetta C
    3. Gurtner A
    4. Illi B
    5. Rosati J
    6. Straino S
    7. Ragone G
    8. Pescatori M
    9. Zaccagnini G
    10. Antonini A
    11. Minetti G
    12. Martelli F
    13. Piaggio G
    14. Gallinari P
    15. Steinkuhler C
    16. Steinkulher C
    17. Clementi E
    18. Dell’Aversana C
    19. Altucci L
    20. Mai A
    21. Capogrossi MC
    22. Puri PL
    23. Gaetano C

    (2008) HDAC2 blockade by nitric oxide and histone deacetylase inhibitors reveals a common target in duchenne muscular dystrophy treatment

    PNAS 105:19183–19187.

    https://doi.org/10.1073/pnas.0805514105

    • PubMed
    • Google Scholar
20. 1. Corona BT
    2. Machingal MA
    3. Criswell T
    4. Vadhavkar M
    5. Dannahower AC
    6. Bergman C
    7. Zhao W
    8. Christ GJ

    (2012) Further development of a tissue engineered muscle repair construct in vitro for enhanced functional recovery following implantation in vivo in a murine model of volumetric muscle loss injury

    Tissue Engineering. Part A 18:1213–1228.

    https://doi.org/10.1089/ten.TEA.2011.0614

    • PubMed
    • Google Scholar
21. 1. Dabaj I
    2. Ferey J
    3. Marguet F
    4. Gilard V
    5. Basset C
    6. Bahri Y
    7. Brehin A-C
    8. Vanhulle C
    9. Leturcq F
    10. Marret S
    11. Laquerrière A
    12. Schmitz-Afonso I
    13. Afonso C
    14. Bekri S
    15. Tebani A

    (2018) Muscle metabolic remodelling patterns in duchenne muscular dystrophy revealed by ultra-high-resolution mass spectrometry imaging

    Scientific Reports 11:1–11.

    https://doi.org/10.1038/s41598-021-81090-1

    • Google Scholar
22. 1. DiMario JX
    2. Uzman A
    3. Strohman RC

    (1991) Fiber regeneration is not persistent in dystrophic (MDX) mouse skeletal muscle

    Developmental Biology 148:314–321.

    https://doi.org/10.1016/0012-1606(91)90340-9

    • PubMed
    • Google Scholar
23. 1. Dobin A
    2. Davis CA
    3. Schlesinger F
    4. Drenkow J
    5. Zaleski C
    6. Jha S
    7. Batut P
    8. Chaisson M
    9. Gingeras TR

    (2013) STAR: ultrafast universal RNA-seq aligner

    Bioinformatics 29:15–21.

    https://doi.org/10.1093/bioinformatics/bts635

    • Google Scholar
24. Software

    1. Domenzain V
    2. Anton M
    3. Sánchez B

    (2020) EcModels: container for enzyme-constrained models, version a2cf0d3

    EcModels.

    https://github.com/SysBioChalmers/ecModels/tree/main/ecHumanGEM/model
25. 1. Doran P
    2. Dowling P
    3. Lohan J
    4. McDonnell K
    5. Poetsch S
    6. Ohlendieck K

    (2004) Subproteomics analysis of ca+-binding proteins demonstrates decreased calsequestrin expression in dystrophic mouse skeletal muscle

    European Journal of Biochemistry 271:3943–3952.

    https://doi.org/10.1111/j.1432-1033.2004.04332.x

    • PubMed
    • Google Scholar
26. 1. Dreyfus J
    2. Georges S
    3. Fanny S
    4. Jean D

    (1956) Enzymatic activities of human muscle: research on the comparative biochemistry of normal and myopathic man, and of the rat

    Clinica Chimica Acta 1:434–449.

    https://doi.org/10.1016/0009-8981(56)90016-X

    • PubMed
    • Google Scholar
27. 1. Duddy W
    2. Duguez S
    3. Johnston H
    4. Cohen TV
    5. Phadke A
    6. Gordish-Dressman H
    7. Nagaraju K
    8. Gnocchi V
    9. Low S
    10. Partridge T

    (2015) Muscular dystrophy in the mdx mouse is a severe myopathy compounded by hypotrophy, hypertrophy and hyperplasia

    Skeletal Muscle 5:16.

    https://doi.org/10.1186/s13395-015-0041-y

    • PubMed
    • Google Scholar
28. 1. Dumont NA
    2. Wang YX
    3. Rudnicki MA

    (2015a) Intrinsic and extrinsic mechanisms regulating satellite cell function

    Development 142:1572–1581.

    https://doi.org/10.1242/dev.114223

    • PubMed
    • Google Scholar
29. 1. Dumont NA
    2. Wang YX
    3. von Maltzahn J
    4. Pasut A
    5. Bentzinger CF
    6. Brun CE
    7. Rudnicki MA

    (2015b) Dystrophin expression in muscle stem cells regulates their polarity and asymmetric division

    Nature Medicine 21:1455–1463.

    https://doi.org/10.1038/nm.3990

    • PubMed
    • Google Scholar
30. 1. Dumont NA
    2. Rudnicki MA

    (2016) Targeting muscle stem cell intrinsic defects to treat duchenne muscular dystrophy

    NPJ Regenerative Medicine 1:16006.

    https://doi.org/10.1038/npjregenmed.2016.6

    • PubMed
    • Google Scholar
31. 1. Durany N
    2. Carreras J
    3. Valentí M
    4. Cámara J
    5. Carreras J

    (2002) Inactivation of phosphoglycerate mutase and creatine kinase isoenzymes in human serum

    Molecular Pathology 55:242–249.

    https://doi.org/10.1136/mp.55.4.242

    • PubMed
    • Google Scholar
32. 1. Emery AEH

    (1977) Muscle histology and creatine kinase levels in the foetus in duchenne muscular dystrophy

    Nature 266:472–473.

    https://doi.org/10.1038/266472a0

    • PubMed
    • Google Scholar
33. 1. ENCODE Project Consortium

    (2004) The encode (encyclopedia of DNA elements) project

    Science 306:636–640.

    https://doi.org/10.1126/science.1105136

    • PubMed
    • Google Scholar
34. 1. Epstein JA
    2. Lam P
    3. Jepeal L
    4. Maas RL
    5. Shapiro DN

    (1995) Pax3 inhibits myogenic differentiation of cultured myoblast cells

    The Journal of Biological Chemistry 270:11719–11722.

    https://doi.org/10.1074/jbc.270.20.11719

    • PubMed
    • Google Scholar
35. 1. Farea M
    2. Rani AQM
    3. Maeta K
    4. Nishio H
    5. Matsuo M

    (2020) Dystrophin dp71ab is monoclonally expressed in human satellite cells and enhances proliferation of myoblast cells

    Scientific Reports 10:17123.

    https://doi.org/10.1038/s41598-020-74157-y

    • PubMed
    • Google Scholar
36. 1. Filippelli RL
    2. Chang NC

    (2021) Empowering muscle stem cells for the treatment of duchenne muscular dystrophy

    Cells, Tissues, Organs 1–14:1–14.

    https://doi.org/10.1159/000514305

    • PubMed
    • Google Scholar
37. 1. Fulco M
    2. Cen Y
    3. Zhao P
    4. Hoffman EP
    5. McBurney MW
    6. Sauve AA
    7. Sartorelli V

    (2008) Glucose restriction inhibits skeletal myoblast differentiation by activating SIRT1 through AMPK-mediated regulation of nampt

    Developmental Cell 14:661–673.

    https://doi.org/10.1016/j.devcel.2008.02.004

    • PubMed
    • Google Scholar
38. 1. Ghahramani Seno MM
    2. Graham IR
    3. Athanasopoulos T
    4. Trollet C
    5. Pohlschmidt M
    6. Crompton MR
    7. Dickson G

    (2008) RNAi-mediated knockdown of dystrophin expression in adult mice does not lead to overt muscular dystrophy pathology

    Human Molecular Genetics 17:2622–2632.

    https://doi.org/10.1093/hmg/ddn162

    • PubMed
    • Google Scholar
39. 1. Gonzalez JP
    2. Kyrychenko S
    3. Kyrychenko V
    4. Schneider JS
    5. Granier CJ
    6. Himelman E
    7. Lahey KC
    8. Zhao Q
    9. Yehia G
    10. Tao YX
    11. Bhaumik M
    12. Shirokova N
    13. Fraidenraich D

    (2017) Small fractions of muscular dystrophy embryonic stem cells yield severe cardiac and skeletal muscle defects in adult mouse chimeras

    Stem Cells 35:597–610.

    https://doi.org/10.1002/stem.2518

    • PubMed
    • Google Scholar
40. 1. Górecki DC

    (2016) Dystrophin: the dead calm of a dogma

    Rare Diseases 4:e1153777.

    https://doi.org/10.1080/21675511.2016.1153777

    • PubMed
    • Google Scholar
41. 1. Gudmundsson S
    2. Thiele I

    (2010) Computationally efficient flux variability analysis

    BMC Bioinformatics 11:1–3.

    https://doi.org/10.1186/1471-2105-11-489

    • Google Scholar
42. 1. Haines BP
    2. Gupta R
    3. Jones CM
    4. Summerbell D
    5. Rigby PWJ

    (2005) The NLRR gene family and mouse development: modified differential display PCR identifies NLRR-1 as a gene expressed in early somitic myoblasts

    Developmental Biology 281:145–159.

    https://doi.org/10.1016/j.ydbio.2005.01.030

    • PubMed
    • Google Scholar
43. 1. Harrer M
    2. Lührs H
    3. Bustin M
    4. Scheer U
    5. Hock R

    (2004) Dynamic interaction of hmga1a proteins with chromatin

    Journal of Cell Science 117:3459–3471.

    https://doi.org/10.1242/jcs.01160

    • PubMed
    • Google Scholar
44. 1. Heirendt L
    2. Arreckx S
    3. Pfau T
    4. Mendoza SN
    5. Richelle A
    6. Heinken A
    7. Haraldsdóttir HS
    8. Wachowiak J
    9. Keating SM
    10. Vlasov V
    11. Magnusdóttir S
    12. Ng CY
    13. Preciat G
    14. Žagare A
    15. Chan SHJ
    16. Aurich MK
    17. Clancy CM
    18. Modamio J
    19. Sauls JT
    20. Noronha A
    21. Bordbar A
    22. Cousins B
    23. El Assal DC
    24. Valcarcel LV
    25. Apaolaza I
    26. Ghaderi S
    27. Ahookhosh M
    28. Ben Guebila M
    29. Kostromins A
    30. Sompairac N
    31. Le HM
    32. Ma D
    33. Sun Y
    34. Wang L
    35. Yurkovich JT
    36. Oliveira MAP
    37. Vuong PT
    38. El Assal LP
    39. Kuperstein I
    40. Zinovyev A
    41. Hinton HS
    42. Bryant WA
    43. Aragón Artacho FJ
    44. Planes FJ
    45. Stalidzans E
    46. Maass A
    47. Vempala S
    48. Hucka M
    49. Saunders MA
    50. Maranas CD
    51. Lewis NE
    52. Sauter T
    53. Palsson BØ
    54. Thiele I
    55. Fleming RMT

    (2019) Creation and analysis of biochemical constraint-based models using the COBRA toolbox v.3.0

    Nature Protocols 14:639–702.

    https://doi.org/10.1038/s41596-018-0098-2

    • PubMed
    • Google Scholar
45. 1. Hildyard JCW
    2. Crawford AH
    3. Rawson F
    4. Riddell DO
    5. Harron RCM
    6. Piercy RJ

    (2020) Single-transcript multiplex in situ hybridisation reveals unique patterns of dystrophin isoform expression in the developing mammalian embryo

    Wellcome Open Research 5:76.

    https://doi.org/10.12688/wellcomeopenres.15762.2

    • PubMed
    • Google Scholar
46. 1. Hoffman EP
    2. Fischbeck KH
    3. Brown RH
    4. Johnson M
    5. Medori R
    6. Loike JD
    7. Harris JB
    8. Waterston R
    9. Brooke M
    10. Specht L

    (1988) Characterization of dystrophin in muscle-biopsy specimens from patients with duchenne’s or becker’s muscular dystrophy

    The New England Journal of Medicine 318:1363–1368.

    https://doi.org/10.1056/NEJM198805263182104

    • PubMed
    • Google Scholar
47. 1. Howard PL
    2. Dally GY
    3. Ditta SD
    4. Austin RC
    5. Worton RG
    6. Klamut HJ
    7. Ray PN

    (1999) Dystrophin isoforms DP71 and DP427 have distinct roles in myogenic cells

    Muscle & Nerve 22:16–27.

    https://doi.org/10.1002/(sici)1097-4598(199901)22:1<16::aid-mus5>3.0.co;2-r

    • PubMed
    • Google Scholar
48. 1. Howard ZM
    2. Lowe J
    3. Blatnik AJ
    4. Roberts D
    5. Burghes AHM
    6. Bansal SS
    7. Rafael-Fortney JA

    (2021) Early inflammation in muscular dystrophy differs between limb and respiratory muscles and increases with dystrophic severity

    The American Journal of Pathology 191:730–747.

    https://doi.org/10.1016/j.ajpath.2021.01.008

    • PubMed
    • Google Scholar
49. 1. Hurko O
    2. McKee L
    3. Zuurveld J
    4. Swick HM

    (1987) Comparison of duchenne and normal myoblasts from a heterozygote

    Neurology 37:675–681.

    https://doi.org/10.1212/wnl.37.4.675

    • PubMed
    • Google Scholar
50. 1. Kamieniarz-Gdula K
    2. Proudfoot NJ

    (2019) Transcriptional control by premature termination: A forgotten mechanism

    Trends in Genetics 35:553–564.

    https://doi.org/10.1016/j.tig.2019.05.005

    • PubMed
    • Google Scholar
51. 1. Kelly-Worden M
    2. Thomas E

    (2014) Mitochondrial dysfunction in duchenne muscular dystrophy

    Open Journal of Endocrine and Metabolic Diseases 04:211–218.

    https://doi.org/10.4236/ojemd.2014.48020

    • Google Scholar
52. 1. Kim S
    2. Lee M-J
    3. Choi J-Y
    4. Park D-H
    5. Kwak H-B
    6. Moon S
    7. Koh J-W
    8. Shin H-K
    9. Ryu J-K
    10. Park C-S
    11. Park JH
    12. Kang J-H

    (2018) Roles of exosome-like vesicles released from inflammatory C2C12 myotubes: regulation of myocyte differentiation and myokine expression

    Cellular Physiology and Biochemistry 48:1829–1842.

    https://doi.org/10.1159/000492505

    • Google Scholar
53. 1. Kim W
    2. Lee H
    3. Lee J
    4. Atala A
    5. Yoo JJ
    6. Lee SJ
    7. Kim GH

    (2020) Efficient myotube formation in 3D bioprinted tissue construct by biochemical and topographical cues

    Biomaterials 230:119632.

    https://doi.org/10.1016/j.biomaterials.2019.119632

    • PubMed
    • Google Scholar
54. Book

    1. Kino Y
    2. Satoh J
    3. Ishiura S

    (2018) Molecular mechanisms of myotonic dystrophy: RNA-mediated pathogenesis and RNA-binding proteins

    In: Takahashi MP, Matsumura T, editors. Myotonic Dystrophy: Disease Mechanism, Current Management and Therapeutic Development. Springer. pp. 19–43.

    https://doi.org/10.1007/978-981-13-0508-5_2

    • Google Scholar
55. 1. Kishioka Y
    2. Thomas M
    3. Wakamatsu J-I
    4. Hattori A
    5. Sharma M
    6. Kambadur R
    7. Nishimura T

    (2008) Decorin enhances the proliferation and differentiation of myogenic cells through suppressing myostatin activity

    Journal of Cellular Physiology 215:856–867.

    https://doi.org/10.1002/jcp.21371

    • PubMed
    • Google Scholar
56. 1. Koeks Z
    2. Bladen CL
    3. Salgado D
    4. van Zwet E
    5. Pogoryelova O
    6. McMacken G
    7. Monges S
    8. Foncuberta ME
    9. Kekou K
    10. Kosma K
    11. Dawkins H
    12. Lamont L
    13. Bellgard MI
    14. Roy AJ
    15. Chamova T
    16. Guergueltcheva V
    17. Chan S
    18. Korngut L
    19. Campbell C
    20. Dai Y
    21. Wang J
    22. Barišić N
    23. Brabec P
    24. Lähdetie J
    25. Walter MC
    26. Schreiber-Katz O
    27. Karcagi V
    28. Garami M
    29. Herczegfalvi A
    30. Viswanathan V
    31. Bayat F
    32. Buccella F
    33. Ferlini A
    34. Kimura E
    35. van den Bergen JC
    36. Rodrigues M
    37. Roxburgh R
    38. Lusakowska A
    39. Kostera-Pruszczyk A
    40. Santos R
    41. Neagu E
    42. Artemieva S
    43. Rasic VM
    44. Vojinovic D
    45. Posada M
    46. Bloetzer C
    47. Klein A
    48. Díaz-Manera J
    49. Gallardo E
    50. Karaduman AA
    51. Oznur T
    52. Topaloğlu H
    53. El Sherif R
    54. Stringer A
    55. Shatillo AV
    56. Martin AS
    57. Peay HL
    58. Kirschner J
    59. Flanigan KM
    60. Straub V
    61. Bushby K
    62. Béroud C
    63. Verschuuren JJ
    64. Lochmüller H

    (2017) Clinical outcomes in duchenne muscular dystrophy: A study of 5345 patients from the TREAT-NMD DMD global database

    Journal of Neuromuscular Diseases 4:293–306.

    https://doi.org/10.3233/JND-170280

    • PubMed
    • Google Scholar
57. 1. Koo JH
    2. Smiley MA
    3. Lovering RM
    4. Margolis FL

    (2007) Bex1 knock out mice show altered skeletal muscle regeneration

    Biochemical and Biophysical Research Communications 363:405–410.

    https://doi.org/10.1016/j.bbrc.2007.08.186

    • PubMed
    • Google Scholar
58. 1. Korotkova M
    2. Lundberg IE

    (2014) The skeletal muscle arachidonic acid cascade in health and inflammatory disease

    Nature Reviews. Rheumatology 10:295–303.

    https://doi.org/10.1038/nrrheum.2014.2

    • PubMed
    • Google Scholar
59. 1. Kreis R
    2. Wingeier K
    3. Vermathen P
    4. Giger E
    5. Joncourt F
    6. Zwygart K
    7. Kaufmann F
    8. Boesch C
    9. Steinlin M

    (2011) Brain metabolite composition in relation to cognitive function and dystrophin mutations in boys with duchenne muscular dystrophy

    NMR in Biomedicine 24:253–262.

    https://doi.org/10.1002/nbm.1582

    • PubMed
    • Google Scholar
60. Software

    1. Krueger F

    (2012) Trim galore! a quality trimming tool for high throughput sequence data, version v3

    Babraham Bioinformatics.

    https://www.bioinformatics.babraham.ac.uk/projects/trim_galore/
61. 1. Lagerwaard B
    2. van der Hoek MD
    3. Hoeks J
    4. Grevendonk L
    5. Nieuwenhuizen AG
    6. Keijer J
    7. de Boer VCJ

    (2021) Propionate hampers differentiation and modifies histone propionylation and acetylation in skeletal muscle cells

    Mechanisms of Ageing and Development 196:111495.

    https://doi.org/10.1016/j.mad.2021.111495

    • PubMed
    • Google Scholar
62. 1. Lawrence M
    2. Huber W
    3. Pagès H
    4. Aboyoun P
    5. Carlson M
    6. Gentleman R
    7. Morgan MT
    8. Carey VJ

    (2013) Software for computing and annotating genomic ranges

    PLOS Computational Biology 9:e1003118.

    https://doi.org/10.1371/journal.pcbi.1003118

    • PubMed
    • Google Scholar
63. 1. Lee-Young RS
    2. Hoffman NJ
    3. Murphy KT
    4. Henstridge DC
    5. Samocha-Bonet D
    6. Siebel AL
    7. Iliades P
    8. Zivanovic B
    9. Hong YH
    10. Colgan TD
    11. Kraakman MJ
    12. Bruce CR
    13. Gregorevic P
    14. McConell GK
    15. Lynch GS
    16. Drummond GR
    17. Kingwell BA
    18. Greenfield JR
    19. Febbraio MA

    (2016) Glucose-6-phosphate dehydrogenase contributes to the regulation of glucose uptake in skeletal muscle

    Molecular Metabolism 5:1083–1091.

    https://doi.org/10.1016/j.molmet.2016.09.002

    • PubMed
    • Google Scholar
64. 1. Li H
    2. Handsaker B
    3. Wysoker A
    4. Fennell T
    5. Ruan J
    6. Homer N
    7. Marth G
    8. Abecasis G
    9. Durbin R
    10. 1000 Genome Project Data Processing Subgroup

    (2009a) The sequence alignment/map format and samtools

    Bioinformatics 25:2078–2079.

    https://doi.org/10.1093/bioinformatics/btp352

    • PubMed
    • Google Scholar
65. 1. Li D
    2. Niu Z
    3. Yu W
    4. Qian Y
    5. Wang Q
    6. Li Q
    7. Yi Z
    8. Luo J
    9. Wu X
    10. Wang Y
    11. Schwartz RJ
    12. Liu M

    (2009b) SMYD1, the myogenic activator, is a direct target of serum response factor and myogenin

    Nucleic Acids Research 37:7059–7071.

    https://doi.org/10.1093/nar/gkp773

    • PubMed
    • Google Scholar
66. 1. Lidov HG
    2. Byers TJ
    3. Watkins SC
    4. Kunkel LM

    (1990) Localization of dystrophin to postsynaptic regions of central nervous system cortical neurons

    Nature 348:725–728.

    https://doi.org/10.1038/348725a0

    • PubMed
    • Google Scholar
67. 1. Lindsay A
    2. Chamberlain CM
    3. Witthuhn BA
    4. Lowe DA
    5. Ervasti JM

    (2019) Dystrophinopathy-associated dysfunction of krebs cycle metabolism

    Human Molecular Genetics 28:942–951.

    https://doi.org/10.1093/hmg/ddy404

    • PubMed
    • Google Scholar
68. 1. Love MI
    2. Huber W
    3. Anders S

    (2014) Moderated estimation of fold change and dispersion for RNA-seq data with deseq2

    Genome Biology 15:550.

    https://doi.org/10.1186/s13059-014-0550-8

    • PubMed
    • Google Scholar
69. 1. Magazzù G
    2. Zampieri G
    3. Angione C

    (2021) Multimodal regularised linear models with flux balance analysis for mechanistic integration of omics data

    Bioinformatics btab324:btab324.

    https://doi.org/10.1093/bioinformatics/btab324

    • PubMed
    • Google Scholar
70. 1. Magli A
    2. Baik J
    3. Mills LJ
    4. Kwak I-Y
    5. Dillon BS
    6. Mondragon Gonzalez R
    7. Stafford DA
    8. Swanson SA
    9. Stewart R
    10. Thomson JA
    11. Garry DJ
    12. Dynlacht BD
    13. Perlingeiro RCR

    (2019) Time-dependent pax3-mediated chromatin remodeling and cooperation with six4 and tead2 specify the skeletal myogenic lineage in developing mesoderm

    PLOS Biology 17:e3000153.

    https://doi.org/10.1371/journal.pbio.3000153

    • PubMed
    • Google Scholar
71. 1. Mal A
    2. Sturniolo M
    3. Schiltz RL
    4. Ghosh MK
    5. Harter ML

    (2001) A role for histone deacetylase HDAC1 in modulating the transcriptional activity of myod: inhibition of the myogenic program

    The EMBO Journal 20:1739–1753.

    https://doi.org/10.1093/emboj/20.7.1739

    • PubMed
    • Google Scholar
72. 1. Mangeot PE
    2. Risson V
    3. Fusil F
    4. Marnef A
    5. Laurent E
    6. Blin J
    7. Mournetas V
    8. Massouridès E
    9. Sohier TJM
    10. Corbin A
    11. Aubé F
    12. Teixeira M
    13. Pinset C
    14. Schaeffer L
    15. Legube G
    16. Cosset F-L
    17. Verhoeyen E
    18. Ohlmann T
    19. Ricci EP

    (2019) Genome editing in primary cells and in vivo using viral-derived nanoblades loaded with cas9-sgrna ribonucleoproteins

    Nature Communications 10:45.

    https://doi.org/10.1038/s41467-018-07845-z

    • PubMed
    • Google Scholar
73. 1. Massopust RT
    2. Lee YI
    3. Pritchard AL
    4. Nguyen V-KM
    5. McCreedy DA
    6. Thompson WJ

    (2020) Lifetime analysis of mdx skeletal muscle reveals a progressive pathology that leads to myofiber loss

    Scientific Reports 10:17248.

    https://doi.org/10.1038/s41598-020-74192-9

    • PubMed
    • Google Scholar
74. 1. Massouridès E
    2. Polentes J
    3. Mangeot P-E
    4. Mournetas V
    5. Nectoux J
    6. Deburgrave N
    7. Nusbaum P
    8. Leturcq F
    9. Popplewell L
    10. Dickson G
    11. Wein N
    12. Flanigan KM
    13. Peschanski M
    14. Chelly J
    15. Pinset C

    (2015) Dp412e: a novel human embryonic dystrophin isoform induced by BMP4 in early differentiated cells

    Skeletal Muscle 5:40.

    https://doi.org/10.1186/s13395-015-0062-6

    • PubMed
    • Google Scholar
75. 1. Matre PR
    2. Mu X
    3. Wu J
    4. Danila D
    5. Hall MA
    6. Kolonin MG
    7. Darabi R
    8. Huard J

    (2019) CRISPR/cas9-based dystrophin restoration reveals a novel role for dystrophin in bioenergetics and stress resistance of muscle progenitors

    Stem Cells 37:1615–1628.

    https://doi.org/10.1002/stem.3094

    • PubMed
    • Google Scholar
76. 1. McGreevy JW
    2. Hakim CH
    3. McIntosh MA
    4. Duan D

    (2015) Animal models of duchenne muscular dystrophy: from basic mechanisms to gene therapy

    Disease Models & Mechanisms 8:195–213.

    https://doi.org/10.1242/dmm.018424

    • PubMed
    • Google Scholar
77. 1. Megeney LA
    2. Kablar B
    3. Perry RL
    4. Ying C
    5. May L
    6. Rudnicki MA

    (1999) Severe cardiomyopathy in mice lacking dystrophin and myod

    PNAS 96:220–225.

    https://doi.org/10.1073/pnas.96.1.220

    • PubMed
    • Google Scholar
78. 1. Mei C
    2. Chao C-W
    3. Lin C-W
    4. Li ST
    5. Wu K-H
    6. Yang K-C
    7. Yu J

    (2019) Three-dimensional spherical gelatin bubble-based scaffold improves the myotube formation of h9c2 myoblasts

    Biotechnology and Bioengineering 116:1190–1200.

    https://doi.org/10.1002/bit.26917

    • PubMed
    • Google Scholar
79. 1. Melone MAB
    2. Peluso G
    3. Petillo O
    4. Umberto G
    5. Cotrufo R

    (2000) Defective growth in vitro of duchenne muscular dystrophy myoblasts: the molecular and biochemical basis

    Journal of Cellular Biochemistry 76:118–132.

    https://doi.org/10.1002/(SICI)1097-4644(20000101)76:1<118::AID-JCB12>3.0.CO;2-F

    • Google Scholar
80. 1. Merrick D
    2. Stadler LKJ
    3. Larner D
    4. Smith J

    (2009) Muscular dystrophy begins early in embryonic development deriving from stem cell loss and disrupted skeletal muscle formation

    Disease Models & Mechanisms 2:374–388.

    https://doi.org/10.1242/dmm.001008

    • PubMed
    • Google Scholar
81. 1. Minetti GC
    2. Colussi C
    3. Adami R
    4. Serra C
    5. Mozzetta C
    6. Parente V
    7. Fortuni S
    8. Straino S
    9. Sampaolesi M
    10. Di Padova M
    11. Illi B
    12. Gallinari P
    13. Steinkühler C
    14. Capogrossi MC
    15. Sartorelli V
    16. Bottinelli R
    17. Gaetano C
    18. Puri PL

    (2006) Functional and morphological recovery of dystrophic muscles in mice treated with deacetylase inhibitors

    Nature Medicine 12:1147–1150.

    https://doi.org/10.1038/nm1479

    • PubMed
    • Google Scholar
82. 1. Mochida Y
    2. Parisuthiman D
    3. Pornprasertsuk-Damrongsri S
    4. Atsawasuwan P
    5. Sricholpech M
    6. Boskey AL
    7. Yamauchi M

    (2009) Decorin modulates collagen matrix assembly and mineralization

    Matrix Biology 28:44–52.

    https://doi.org/10.1016/j.matbio.2008.11.003

    • PubMed
    • Google Scholar
83. 1. Morgan JE
    2. Beauchamp JR
    3. Pagel CN
    4. Peckham M
    5. Ataliotis P
    6. Jat PS
    7. Noble MD
    8. Farmer K
    9. Partridge TA

    (1994) Myogenic cell lines derived from transgenic mice carrying a thermolabile T antigen: a model system for the derivation of tissue-specific and mutation-specific cell lines

    Developmental Biology 162:486–498.

    https://doi.org/10.1006/dbio.1994.1103

    • PubMed
    • Google Scholar
84. 1. Mournetas V
    2. Massouridès E
    3. Dupont J-B
    4. Kornobis E
    5. Polvèche H
    6. Jarrige M
    7. Dorval ARL
    8. Gosselin MRF
    9. Manousopoulou A
    10. Garbis SD
    11. Górecki DC
    12. Pinset C

    (2021) Myogenesis modelled by human pluripotent stem cells: a multi-omic study of duchenne myopathy early onset

    Journal of Cachexia, Sarcopenia and Muscle 12:209–232.

    https://doi.org/10.1002/jcsm.12665

    • PubMed
    • Google Scholar
85. 1. Nesmith AP
    2. Wagner MA
    3. Pasqualini FS
    4. O’Connor BB
    5. Pincus MJ
    6. August PR
    7. Parker KK

    (2016) A human in vitro model of duchenne muscular dystrophy muscle formation and contractility

    The Journal of Cell Biology 215:47–56.

    https://doi.org/10.1083/jcb.201603111

    • PubMed
    • Google Scholar
86. 1. Nguyen F
    2. Cherel Y
    3. Guigand L
    4. Goubault-Leroux I
    5. Wyers M

    (2002) Muscle lesions associated with dystrophin deficiency in neonatal golden retriever puppies

    Journal of Comparative Pathology 126:100–108.

    https://doi.org/10.1053/jcpa.2001.0526

    • PubMed
    • Google Scholar
87. 1. Nsiah-Sefaa A
    2. McKenzie M

    (2016) Combined defects in oxidative phosphorylation and fatty acid β-oxidation in mitochondrial disease

    Bioscience Reports 36:e00313.

    https://doi.org/10.1042/BSR20150295

    • PubMed
    • Google Scholar
88. 1. Onopiuk M
    2. Brutkowski W
    3. Wierzbicka K
    4. Wojciechowska S
    5. Szczepanowska J
    6. Fronk J
    7. Lochmüller H
    8. Górecki DC
    9. Zabłocki K

    (2009) Mutation in dystrophin-encoding gene affects energy metabolism in mouse myoblasts

    Biochemical and Biophysical Research Communications 386:463–466.

    https://doi.org/10.1016/j.bbrc.2009.06.053

    • PubMed
    • Google Scholar
89. 1. Onopiuk M
    2. Brutkowski W
    3. Young C
    4. Krasowska E
    5. Róg J
    6. Ritso M
    7. Wojciechowska S
    8. Arkle S
    9. Zabłocki K
    10. Górecki DC

    (2015) Store-operated calcium entry contributes to abnormal ca2+ signalling in dystrophic mdx mouse myoblasts

    Archives of Biochemistry and Biophysics 569:1–9.

    https://doi.org/10.1016/j.abb.2015.01.025

    • PubMed
    • Google Scholar
90. 1. Otto T
    2. Candido SV
    3. Pilarz MS
    4. Sicinska E
    5. Bronson RT
    6. Bowden M
    7. Lachowicz IA
    8. Mulry K
    9. Fassl A
    10. Han RC
    11. Jecrois ES
    12. Sicinski P

    (2017) Cell cycle-targeting micrornas promote differentiation by enforcing cell-cycle exit

    PNAS 114:10660–10665.

    https://doi.org/10.1073/pnas.1702914114

    • PubMed
    • Google Scholar
91. 1. Pant M
    2. Sopariwala DH
    3. Bal NC
    4. Lowe J
    5. Delfín DA
    6. Rafael-Fortney J
    7. Periasamy M

    (2015) Metabolic dysfunction and altered mitochondrial dynamics in the utrophin-dystrophin deficient mouse model of duchenne muscular dystrophy

    PLOS ONE 10:e0123875.

    https://doi.org/10.1371/journal.pone.0123875

    • PubMed
    • Google Scholar
92. 1. Percival JM
    2. Whitehead NP
    3. Adams ME
    4. Adamo CM
    5. Beavo JA
    6. Froehner SC

    (2012) Sildenafil reduces respiratory muscle weakness and fibrosis in the mdx mouse model of duchenne muscular dystrophy

    The Journal of Pathology 228:77–87.

    https://doi.org/10.1002/path.4054

    • PubMed
    • Google Scholar
93. 1. Pescatori M
    2. Broccolini A
    3. Minetti C
    4. Bertini E
    5. Bruno C
    6. D’amico A
    7. Bernardini C
    8. Mirabella M
    9. Silvestri G
    10. Giglio V
    11. Modoni A
    12. Pedemonte M
    13. Tasca G
    14. Galluzzi G
    15. Mercuri E
    16. Tonali PA
    17. Ricci E

    (2007) Gene expression profiling in the early phases of DMD: a constant molecular signature characterizes DMD muscle from early postnatal life throughout disease progression

    FASEB Journal 21:1210–1226.

    https://doi.org/10.1096/fj.06-7285com

    • PubMed
    • Google Scholar
94. 1. Petkov PM
    2. Ding Y
    3. Cassell MA
    4. Zhang W
    5. Wagner G
    6. Sargent EE
    7. Asquith S
    8. Crew V
    9. Johnson KA
    10. Robinson P
    11. Scott VE
    12. Wiles MV

    (2004) An efficient SNP system for mouse genome scanning and elucidating strain relationships

    Genome Research 14:1806–1811.

    https://doi.org/10.1101/gr.2825804

    • PubMed
    • Google Scholar
95. 1. Piechota M
    2. Korostynski M
    3. Ficek J
    4. Tomski A
    5. Przewlocki R

    (2016) Seqinspector: position-based navigation through the chip-seq data landscape to identify gene expression regulators

    BMC Bioinformatics 17:85.

    https://doi.org/10.1186/s12859-016-0938-4

    • PubMed
    • Google Scholar
96. 1. Qin CY
    2. Cai H
    3. Qing HR
    4. Li L
    5. Zhang HP

    (2017) Recent advances on the role of long non-coding RNA H19 in regulating mammalian muscle growth and development

    Yi Chuan = Hereditas 39:1150–1157.

    https://doi.org/10.16288/j.yczz.17-193

    • PubMed
    • Google Scholar
97. 1. Qiu H
    2. Zhong J
    3. Luo L
    4. Tang Z
    5. Liu N
    6. Kang K
    7. Li L
    8. Gou D

    (2017) Regulatory axis of mir-195/497 and HMGA1-id3 governs muscle cell proliferation and differentiation

    International Journal of Biological Sciences 13:157–166.

    https://doi.org/10.7150/ijbs.17440

    • PubMed
    • Google Scholar
98. 1. Rader EP
    2. Turk R
    3. Willer T
    4. Beltrán D
    5. Inamori KI
    6. Peterson TA
    7. Engle J
    8. Prouty S
    9. Matsumura K
    10. Saito F
    11. Anderson ME
    12. Campbell KP

    (2016) Role of dystroglycan in limiting contraction-induced injury to the sarcomeric cytoskeleton of mature skeletal muscle

    PNAS 113:10992–10997.

    https://doi.org/10.1073/pnas.1605265113

    • PubMed
    • Google Scholar
99. 1. Ragusa RJ
    2. Chow CK
    3. Porter JD

    (1997) Oxidative stress as a potential pathogenic mechanism in an animal model of duchenne muscular dystrophy

    Neuromuscular Disorders 7:379–386.

    https://doi.org/10.1016/S0960-8966(97)00096-5

    • Google Scholar
100. 1. Rapaport D
     2. Fuchs O
     3. Nudel U
     4. Yaffe D

     (1992)

     Expression of the duchenne muscular dystrophy gene products in embryonic stem cells and their differentiated derivatives

     The Journal of Biological Chemistry 267:21289–21292.

     • PubMed
     • Google Scholar
101. 1. Rawls KD
     2. Dougherty BV
     3. Blais EM
     4. Stancliffe E
     5. Kolling GL
     6. Vinnakota K
     7. Pannala VR
     8. Wallqvist A
     9. Papin JA

     (2019) A simplified metabolic network reconstruction to promote understanding and development of flux balance analysis tools

     Computers in Biology and Medicine 105:64–71.

     https://doi.org/10.1016/j.compbiomed.2018.12.010

     • PubMed
     • Google Scholar
102. Software

     1. R Development Core Team

     (2021) R: A language and environment for statistical computing

     R Foundation for Statistical Computing, Vienna, Austria.

     https://www.r-project.org/index.html
103. 1. Ribeiro AF Jr
     2. Souza LS
     3. Almeida CF
     4. Ishiba R
     5. Fernandes SA
     6. Guerrieri DA
     7. Santos ALF
     8. Onofre-Oliveira PCG
     9. Vainzof M

     (2019) Muscle satellite cells and impaired late stage regeneration in different murine models for muscular dystrophies

     Scientific Reports 9:11842.

     https://doi.org/10.1038/s41598-019-48156-7

     • PubMed
     • Google Scholar
104. 1. Robinson JL
     2. Kocabaş P
     3. Wang H
     4. Cholley P-E
     5. Cook D
     6. Nilsson A
     7. Anton M
     8. Ferreira R
     9. Domenzain I
     10. Billa V
     11. Limeta A
     12. Hedin A
     13. Gustafsson J
     14. Kerkhoven EJ
     15. Svensson LT
     16. Palsson BO
     17. Mardinoglu A
     18. Hansson L
     19. Uhlén M
     20. Nielsen J

     (2020) An atlas of human metabolism

     Science Signaling 13:eaaz1482.

     https://doi.org/10.1126/scisignal.aaz1482

     • PubMed
     • Google Scholar
105. 1. Rodrigues M
     2. Yokota T

     (2018) An overview of recent advances and clinical applications of exon skipping and splice modulation for muscular dystrophy and various genetic diseases

     Methods in Molecular Biology 1828:31–55.

     https://doi.org/10.1007/978-1-4939-8651-4_2

     • PubMed
     • Google Scholar
106. 1. Róg J
     2. Oksiejuk A
     3. Gosselin MRF
     4. Brutkowski W
     5. Dymkowska D
     6. Nowak N
     7. Robson S
     8. Górecki DC
     9. Zabłocki K

     (2019) Dystrophic mdx mouse myoblasts exhibit elevated ATP/UTP-evoked metabotropic purinergic responses and alterations in calcium signalling

     Biochimica et Biophysica Acta - Molecular Basis of Disease 1865:1138–1151.

     https://doi.org/10.1016/j.bbadis.2019.01.002

     • Google Scholar
107. 1. Sacco A
     2. Mourkioti F
     3. Tran R
     4. Choi J
     5. Llewellyn M
     6. Kraft P
     7. Shkreli M
     8. Delp S
     9. Pomerantz JH
     10. Artandi SE
     11. Blau HM

     (2010) Short telomeres and stem cell exhaustion model duchenne muscular dystrophy in mdx/mtr mice

     Cell 143:1059–1071.

     https://doi.org/10.1016/j.cell.2010.11.039

     • PubMed
     • Google Scholar
108. 1. Saccone V
     2. Consalvi S
     3. Giordani L
     4. Mozzetta C
     5. Barozzi I
     6. Sandoná M
     7. Ryan T
     8. Rojas-Muñoz A
     9. Madaro L
     10. Fasanaro P
     11. Borsellino G
     12. De Bardi M
     13. Frigè G
     14. Termanini A
     15. Sun X
     16. Rossant J
     17. Bruneau BG
     18. Mercola M
     19. Minucci S
     20. Puri PL

     (2014) HDAC-regulated myomirs control BAF60 variant exchange and direct the functional phenotype of fibro-adipogenic progenitors in dystrophic muscles

     Genes & Development 28:841–857.

     https://doi.org/10.1101/gad.234468.113

     • PubMed
     • Google Scholar
109. 1. Sakamoto K
     2. Yamaguchi S
     3. Ando R
     4. Miyawaki A
     5. Kabasawa Y
     6. Takagi M
     7. Li CL
     8. Perbal B
     9. Katsube K

     (2002) The nephroblastoma overexpressed gene (NOV/ccn3) protein associates with notch1 extracellular domain and inhibits myoblast differentiation via notch signaling pathway

     The Journal of Biological Chemistry 277:29399–29405.

     https://doi.org/10.1074/jbc.M203727200

     • PubMed
     • Google Scholar
110. 1. Sánchez BJ
     2. Zhang C
     3. Nilsson A
     4. Lahtvee P-J
     5. Kerkhoven EJ
     6. Nielsen J

     (2017) Improving the phenotype predictions of a yeast genome-scale metabolic model by incorporating enzymatic constraints

     Molecular Systems Biology 13:935.

     https://doi.org/10.15252/msb.20167411

     • PubMed
     • Google Scholar
111. 1. Scholte HR
     2. Busch HF

     (1980) Early changes of muscle mitochondria in duchenne dystrophy: partition and activity of mitochondrial enzymes in fractionated muscle of unaffected boys and adults and patients

     Journal of the Neurological Sciences 45:217–234.

     https://doi.org/10.1016/0022-510x(80)90167-7

     • PubMed
     • Google Scholar
112. 1. Schuierer MM
     2. Mann CJ
     3. Bildsoe H
     4. Huxley C
     5. Hughes SM

     (2005) Analyses of the differentiation potential of satellite cells from myod-/-, mdx, and PMP22 C22 mice

     BMC Musculoskeletal Disorders 6:15.

     https://doi.org/10.1186/1471-2474-6-15

     • PubMed
     • Google Scholar
113. 1. Scionti I
     2. Hayashi S
     3. Mouradian S
     4. Girard E
     5. Esteves de Lima J
     6. Morel V
     7. Simonet T
     8. Wurmser M
     9. Maire P
     10. Ancelin K
     11. Metzger E
     12. Schüle R
     13. Goillot E
     14. Relaix F
     15. Schaeffer L

     (2017) LSD1 controls timely myod expression via myod core enhancer transcription

     Cell Reports 18:1996–2006.

     https://doi.org/10.1016/j.celrep.2017.01.078

     • PubMed
     • Google Scholar
114. 1. Sharma D
     2. Al-Khalidi R
     3. Edgar S
     4. An Q
     5. Wang Y
     6. Young C
     7. Nowis D
     8. Gorecki DC

     (2017) Co-delivery of indoleamine 2,3-dioxygenase prevents loss of expression of an antigenic transgene in dystrophic mouse muscles

     Gene Therapy 24:113–119.

     https://doi.org/10.1038/gt.2016.82

     • PubMed
     • Google Scholar
115. 1. Shoji E
     2. Sakurai H
     3. Nishino T
     4. Nakahata T
     5. Heike T
     6. Awaya T
     7. Fujii N
     8. Manabe Y
     9. Matsuo M
     10. Sehara-Fujisawa A

     (2015) Early pathogenesis of duchenne muscular dystrophy modelled in patient-derived human induced pluripotent stem cells

     Scientific Reports 5:12831.

     https://doi.org/10.1038/srep12831

     • PubMed
     • Google Scholar
116. 1. Sicinski P
     2. Geng Y
     3. Ryder-Cook AS
     4. Barnard EA
     5. Darlison MG
     6. Barnard PJ

     (1989) The molecular basis of muscular dystrophy in the mdx mouse: a point mutation

     Science 244:1578–1580.

     https://doi.org/10.1126/science.2662404

     • PubMed
     • Google Scholar
117. 1. Sinadinos A
     2. Young CNJ
     3. Al-Khalidi R
     4. Teti A
     5. Kalinski P
     6. Mohamad S
     7. Floriot L
     8. Henry T
     9. Tozzi G
     10. Jiang T
     11. Wurtz O
     12. Lefebvre A
     13. Shugay M
     14. Tong J
     15. Vaudry D
     16. Arkle S
     17. doRego JC
     18. Górecki DC

     (2015) P2RX7 purinoceptor: a therapeutic target for ameliorating the symptoms of duchenne muscular dystrophy

     PLOS Medicine 12:e1001888.

     https://doi.org/10.1371/journal.pmed.1001888

     • PubMed
     • Google Scholar
118. 1. Srivastava N
     2. Mukherjee S
     3. Mishra V

     (2020) Metabolic disturbance in patients with muscular dystrophy and reflection of altered enzyme activity in dystrophic muscle: one critical view

     Journal of Biomedical Research & Environmental Sciences 1:393–403.

     https://doi.org/10.37871/jbres1171

     • Google Scholar
119. 1. Stern-Straeter J
     2. Bonaterra GA
     3. Kassner SS
     4. Zügel S
     5. Hörmann K
     6. Kinscherf R
     7. Goessler UR

     (2011) Characterization of human myoblast differentiation for tissue-engineering purposes by quantitative gene expression analysis

     Journal of Tissue Engineering and Regenerative Medicine 5:e197–e206.

     https://doi.org/10.1002/term.417

     • PubMed
     • Google Scholar
120. 1. Sudmant PH
     2. Rausch T
     3. Gardner EJ
     4. Handsaker RE
     5. Abyzov A
     6. Huddleston J
     7. Zhang Y
     8. Ye K
     9. Jun G
     10. Fritz MH-Y
     11. Konkel MK
     12. Malhotra A
     13. Stütz AM
     14. Shi X
     15. Casale FP
     16. Chen J
     17. Hormozdiari F
     18. Dayama G
     19. Chen K
     20. Malig M
     21. Chaisson MJP
     22. Walter K
     23. Meiers S
     24. Kashin S
     25. Garrison E
     26. Auton A
     27. Lam HYK
     28. Mu XJ
     29. Alkan C
     30. Antaki D
     31. Bae T
     32. Cerveira E
     33. Chines P
     34. Chong Z
     35. Clarke L
     36. Dal E
     37. Ding L
     38. Emery S
     39. Fan X
     40. Gujral M
     41. Kahveci F
     42. Kidd JM
     43. Kong Y
     44. Lameijer E-W
     45. McCarthy S
     46. Flicek P
     47. Gibbs RA
     48. Marth G
     49. Mason CE
     50. Menelaou A
     51. Muzny DM
     52. Nelson BJ
     53. Noor A
     54. Parrish NF
     55. Pendleton M
     56. Quitadamo A
     57. Raeder B
     58. Schadt EE
     59. Romanovitch M
     60. Schlattl A
     61. Sebra R
     62. Shabalin AA
     63. Untergasser A
     64. Walker JA
     65. Wang M
     66. Yu F
     67. Zhang C
     68. Zhang J
     69. Zheng-Bradley X
     70. Zhou W
     71. Zichner T
     72. Sebat J
     73. Batzer MA
     74. McCarroll SA
     75. 1000 Genomes Project Consortium
     76. Mills RE
     77. Gerstein MB
     78. Bashir A
     79. Stegle O
     80. Devine SE
     81. Lee C
     82. Eichler EE
     83. Korbel JO

     (2015) An integrated map of structural variation in 2,504 human genomes

     Nature 526:75–81.

     https://doi.org/10.1038/nature15394

     • PubMed
     • Google Scholar
121. 1. Sun C
     2. Choi IY
     3. Rovira Gonzalez YI
     4. Andersen P
     5. Talbot CC Jr
     6. Iyer SR
     7. Lovering RM
     8. Wagner KR
     9. Lee G

     (2020) Duchenne muscular dystrophy hipsc-derived myoblast drug screen identifies compounds that ameliorate disease in mdx mice

     JCI Insight 5:134287.

     https://doi.org/10.1172/jci.insight.134287

     • PubMed
     • Google Scholar
122. 1. Supek F
     2. Bošnjak M
     3. Škunca N
     4. Šmuc T

     (2011) REVIGO summarizes and visualizes long lists of gene ontology terms

     PLOS ONE 6:e21800.

     https://doi.org/10.1371/journal.pone.0021800

     • PubMed
     • Google Scholar
123. 1. Szklarczyk D
     2. Gable AL
     3. Lyon D
     4. Junge A
     5. Wyder S
     6. Huerta-Cepas J
     7. Simonovic M
     8. Doncheva NT
     9. Morris JH
     10. Bork P
     11. Jensen LJ
     12. Mering C

     (2019) STRING v11: protein-protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets

     Nucleic Acids Research 47:D607–D613.

     https://doi.org/10.1093/nar/gky1131

     • PubMed
     • Google Scholar
124. 1. Tan X
     2. Rotllant J
     3. Li H
     4. De Deyne P
     5. DeDeyne P
     6. Du SJ

     (2006) SmyD1, a histone methyltransferase, is required for myofibril organization and muscle contraction in zebrafish embryos

     PNAS 103:2713–2718.

     https://doi.org/10.1073/pnas.0509503103

     • PubMed
     • Google Scholar
125. 1. Tennyson CN
     2. Klamut HJ
     3. Worton RG

     (1995) The human dystrophin gene requires 16 hours to be transcribed and is cotranscriptionally spliced

     Nature Genetics 9:184–190.

     https://doi.org/10.1038/ng0295-184

     • PubMed
     • Google Scholar
126. 1. Tidball JG

     (2017) Regulation of muscle growth and regeneration by the immune system

     Nature Reviews. Immunology 17:165–178.

     https://doi.org/10.1038/nri.2016.150

     • PubMed
     • Google Scholar
127. 1. Timpani CA
     2. Hayes A
     3. Rybalka E

     (2015) Revisiting the dystrophin-ATP connection: how half a century of research still implicates mitochondrial dysfunction in duchenne muscular dystrophy aetiology

     Medical Hypotheses 85:1021–1033.

     https://doi.org/10.1016/j.mehy.2015.08.015

     • PubMed
     • Google Scholar
128. 1. Toop J
     2. Emery AEH

     (1977) Muscle histology in fetuses at risk for duchenne muscular dystrophy

     Clinical Genetics 5:230–233.

     https://doi.org/10.1111/j.1399-0004.1974.tb01687.x

     • Google Scholar
129. 1. Tosic M
     2. Allen A
     3. Willmann D
     4. Lepper C
     5. Kim J
     6. Duteil D
     7. Schüle R

     (2018) Lsd1 regulates skeletal muscle regeneration and directs the fate of satellite cells

     Nature Communications 9:366.

     https://doi.org/10.1038/s41467-017-02740-5

     • PubMed
     • Google Scholar
130. 1. Tucci V
     2. Isles AR
     3. Kelsey G
     4. Ferguson-Smith AC

     (2019) Erice imprinting group

     Genomic Imprinting and Physiological Processes in Mammals. Cell 176:952–965.

     https://doi.org/10.1016/j.cell.2019.01.043

     • PubMed
     • Google Scholar
131. 1. Vallejo-Illarramendi A
     2. Toral-Ojeda I
     3. Aldanondo G
     4. López de Munain A

     (2014) Dysregulation of calcium homeostasis in muscular dystrophies

     Expert Reviews in Molecular Medicine 16:e16.

     https://doi.org/10.1017/erm.2014.17

     • PubMed
     • Google Scholar
132. 1. van Dommelen P
     2. van Dijk O
     3. de Wilde JA
     4. Verkerk PH

     (2020) Early developmental milestones in duchenne muscular dystrophy

     Developmental Medicine and Child Neurology 62:1198–1204.

     https://doi.org/10.1111/dmcn.14623

     • PubMed
     • Google Scholar
133. 1. Vassilopoulos D
     2. Emery AE

     (1977) Muscle nuclear changes in fetuses at risk for duchenne muscular dystrophy

     Journal of Medical Genetics 14:13–15.

     https://doi.org/10.1136/jmg.14.1.13

     • PubMed
     • Google Scholar
134. 1. Vendelbo MH
     2. Møller AB
     3. Christensen B
     4. Nellemann B
     5. Clasen BFF
     6. Nair KS
     7. Jørgensen JOL
     8. Jessen N
     9. Møller N
     10. Trajkovic V

     (2014) Fasting increases human skeletal muscle net phenylalanine release and this is associated with decreased mtor signaling

     PLOS ONE 9:e102031.

     https://doi.org/10.1371/journal.pone.0102031

     • Google Scholar
135. 1. Vidal B
     2. Ardite E
     3. Suelves M
     4. Ruiz-Bonilla V
     5. Janué A
     6. Flick MJ
     7. Degen JL
     8. Serrano AL
     9. Muñoz-Cánoves P

     (2012) Amelioration of duchenne muscular dystrophy in mdx mice by elimination of matrix-associated fibrin-driven inflammation coupled to the αMβ2 leukocyte integrin receptor

     Human Molecular Genetics 21:1989–2004.

     https://doi.org/10.1093/hmg/dds012

     • PubMed
     • Google Scholar
136. 1. Vieira NM
     2. Elvers I
     3. Alexander MS
     4. Moreira YB
     5. Eran A
     6. Gomes JP
     7. Marshall JL
     8. Karlsson EK
     9. Verjovski-Almeida S
     10. Lindblad-Toh K
     11. Kunkel LM
     12. Zatz M

     (2015) Jagged 1 rescues the duchenne muscular dystrophy phenotype

     Cell 163:1204–1213.

     https://doi.org/10.1016/j.cell.2015.10.049

     • PubMed
     • Google Scholar
137. 1. Vijayakumar S
     2. Max C
     3. Pietro L
     4. Claudio A

     (2018) Optimization of multi-omic genome-scale models: methodologies, hands-on tutorial, and perspectives

     Metabolic Network Reconstruction and Modeling 1716:389–408.

     https://doi.org/10.1007/978-1-4939-7528-0_18

     • Google Scholar
138. 1. Vila MC
     2. Novak JS
     3. Benny Klimek M
     4. Li N
     5. Morales M
     6. Fritz AG
     7. Edwards K
     8. Boehler JF
     9. Hogarth MW
     10. Kinder TB
     11. Zhang A
     12. Mazala D
     13. Fiorillo AA
     14. Douglas B
     15. Chen Y-W
     16. van den Anker J
     17. Lu QL
     18. Hathout Y
     19. Hoffman EP
     20. Partridge TA
     21. Nagaraju K

     (2019) Morpholino-induced exon skipping stimulates cell-mediated and humoral responses to dystrophin in mdx mice

     The Journal of Pathology 248:339–351.

     https://doi.org/10.1002/path.5263

     • PubMed
     • Google Scholar
139. 1. Wang Y
     2. Marino-Enriquez A
     3. Bennett RR
     4. Zhu M
     5. Shen Y
     6. Eilers G
     7. Lee J-C
     8. Henze J
     9. Fletcher BS
     10. Gu Z
     11. Fox EA
     12. Antonescu CR
     13. Fletcher CDM
     14. Guo X
     15. Raut CP
     16. Demetri GD
     17. van de Rijn M
     18. Ordog T
     19. Kunkel LM
     20. Fletcher JA

     (2014) Dystrophin is a tumor suppressor in human cancers with myogenic programs

     Nature Genetics 46:601–606.

     https://doi.org/10.1038/ng.2974

     • PubMed
     • Google Scholar
140. 1. Wang YX
     2. Feige P
     3. Brun CE
     4. Hekmatnejad B
     5. Dumont NA
     6. Renaud J-M
     7. Faulkes S
     8. Guindon DE
     9. Rudnicki MA

     (2019) EGFR-aurka signaling rescues polarity and regeneration defects in dystrophin-deficient muscle stem cells by increasing asymmetric divisions

     Cell Stem Cell 24:419–432.

     https://doi.org/10.1016/j.stem.2019.01.002

     • Google Scholar
141. 1. Waugh TA
     2. Horstick E
     3. Hur J
     4. Jackson SW
     5. Davidson AE
     6. Li X
     7. Dowling JJ

     (2014) Fluoxetine prevents dystrophic changes in a zebrafish model of duchenne muscular dystrophy

     Human Molecular Genetics 23:4651–4662.

     https://doi.org/10.1093/hmg/ddu185

     • PubMed
     • Google Scholar
142. 1. Wertz K
     2. Füchtbauer EM

     (1998) Dmd(mdx-beta geo): A new allele for the mouse dystrophin gene

     Developmental Dynamics 212:229–241.

     https://doi.org/10.1002/(SICI)1097-0177(199806)212:2<229::AID-AJA7>3.0.CO;2-J

     • PubMed
     • Google Scholar
143. 1. Witkowski JA
     2. Dubowitz V

     (1985) Duchenne muscular dystrophy: studies of cell motility in vitro

     Journal of Cell Science 76:225–234.

     https://doi.org/10.1242/jcs.76.1.225

     • PubMed
     • Google Scholar
144. 1. Xie X
     2. Tsai SY
     3. Tsai MJ

     (2016) COUP-TFII regulates satellite cell function and muscular dystrophy

     The Journal of Clinical Investigation 126:3929–3941.

     https://doi.org/10.1172/JCI87414

     • PubMed
     • Google Scholar
145. 1. Yablonka-Reuveni Z
     2. Anderson JE

     (2006) Satellite cells from dystrophic (mdx) mice display accelerated differentiation in primary cultures and in isolated myofibers

     Developmental Dynamics 235:203–212.

     https://doi.org/10.1002/dvdy.20602

     • PubMed
     • Google Scholar
146. 1. Yaneske E
     2. Zampieri G
     3. Bertoldi L
     4. Benvenuto G
     5. Angione C

     (2021) Genome-scale metabolic modelling of SARS-cov-2 in cancer cells reveals an increased shift to glycolytic energy production

     FEBS Letters 595:2350–2365.

     https://doi.org/10.1002/1873-3468.14180

     • PubMed
     • Google Scholar
147. 1. Yeung D
     2. Zablocki K
     3. Lien C-F
     4. Jiang T
     5. Arkle S
     6. Brutkowski W
     7. Brown J
     8. Lochmuller H
     9. Simon J
     10. Barnard EA
     11. Górecki DC

     (2006) Increased susceptibility to ATP via alteration of P2X receptor function in dystrophic mdx mouse muscle cells

     FASEB Journal 20:610–620.

     https://doi.org/10.1096/fj.05-4022com

     • PubMed
     • Google Scholar
148. 1. Young CNJ
     2. Brutkowski W
     3. Lien C-F
     4. Arkle S
     5. Lochmüller H
     6. Zabłocki K
     7. Górecki DC

     (2012) P2X7 purinoceptor alterations in dystrophic mdx mouse muscles: relationship to pathology and potential target for treatment

     Journal of Cellular and Molecular Medicine 16:1026–1037.

     https://doi.org/10.1111/j.1582-4934.2011.01397.x

     • PubMed
     • Google Scholar
149. 1. Young CNJ
     2. Sinadinos A
     3. Lefebvre A
     4. Chan P
     5. Arkle S
     6. Vaudry D
     7. Gorecki DC

     (2015) A novel mechanism of autophagic cell death in dystrophic muscle regulated by P2RX7 receptor large-pore formation and HSP90

     Autophagy 11:113–130.

     https://doi.org/10.4161/15548627.2014.994402

     • PubMed
     • Google Scholar
150. 1. Young CNJ
     2. Chira N
     3. Róg J
     4. Al-Khalidi R
     5. Benard M
     6. Galas L
     7. Chan P
     8. Vaudry D
     9. Zablocki K
     10. Górecki DC

     (2018) Sustained activation of P2X7 induces MMP-2-evoked cleavage and functional purinoceptor inhibition

     Journal of Molecular Cell Biology 10:229–242.

     https://doi.org/10.1093/jmcb/mjx030

     • PubMed
     • Google Scholar
151. 1. Young CNJ
     2. Gosselin MRF
     3. Rumney R
     4. Oksiejuk A
     5. Chira N
     6. Bozycki L
     7. Matryba P
     8. Łukasiewicz K
     9. Kao AP
     10. Dunlop J
     11. Robson SC
     12. Zabłocki K
     13. Górecki DC

     (2020) Total absence of dystrophin expression exacerbates ectopic myofiber calcification and fibrosis and alters macrophage infiltration patterns

     The American Journal of Pathology 190:190–205.

     https://doi.org/10.1016/j.ajpath.2019.09.021

     • PubMed
     • Google Scholar
152. 1. Yu G
     2. Wang LG
     3. Han Y
     4. He QY

     (2012) ClusterProfiler: an R package for comparing biological themes among gene clusters

     Omics 16:284–287.

     https://doi.org/10.1089/omi.2011.0118

     • PubMed
     • Google Scholar
153. 1. Zampieri G
     2. Vijayakumar S
     3. Yaneske E
     4. Angione C

     (2019) Machine and deep learning meet genome-scale metabolic modeling

     PLOS Computational Biology 15:e1007084.

     https://doi.org/10.1371/journal.pcbi.1007084

     • PubMed
     • Google Scholar
154. 1. Zhang H
     2. Wen J
     3. Bigot A
     4. Chen J
     5. Shang R
     6. Mouly V
     7. Bi P

     (2020) Human myotube formation is determined by myod-myomixer/myomaker axis

     Science Advances 6:eabc4062.

     https://doi.org/10.1126/sciadv.abc4062

     • PubMed
     • Google Scholar
155. 1. Zoghbi HY
     2. Beaudet ALE
     3. Disease H

     (2016) Cold spring harb

     Perspect. Biol 8:a019497.

     https://doi.org/10.1101/cshperspect.a019497

     • Google Scholar
156. 1. Zurlo F
     2. Larson K
     3. Bogardus C
     4. Ravussin E

     (1990) Skeletal muscle metabolism is a major determinant of resting energy expenditure

     The Journal of Clinical Investigation 86:1423–1427.

     https://doi.org/10.1172/JCI114857

     • PubMed
     • Google Scholar

Author details

1. Maxime RF Gosselin

   School of Pharmacy and Biomedical Sciences, University of Portsmouth, Portsmouth, United Kingdom

   Contribution

   Investigation, Writing – original draft, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-8916-0953

2. Virginie Mournetas

   INSERM UEVE UMR861, I-STEM, AFM, Corbeil-Essonnes, France

   Contribution

   Formal analysis, Investigation, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-6557-4190

3. Malgorzata Borczyk

   Laboratory of Pharmacogenomics, Maj Institute of Pharmacology PAS, Krakow, Poland

   Contribution

   Formal analysis, Investigation, Writing – review and editing

   Competing interests

   No competing interests declared

4. Suraj Verma

   School of Computing, Engineering and Digital Technologies, Teesside University, Middlesbrough, United Kingdom

   Contribution

   Formal analysis, Investigation, Writing – original draft

   Competing interests

   No competing interests declared

5. Annalisa Occhipinti

   School of Computing, Engineering and Digital Technologies, Teesside University, Middlesbrough, United Kingdom

   Contribution

   Investigation, Formal analysis

   Competing interests

   No competing interests declared

6. Justyna Róg

   1. School of Pharmacy and Biomedical Sciences, University of Portsmouth, Portsmouth, United Kingdom
   2. Laboratory of Cellular Metabolism, Nencki Institute of Experimental Biology, Warsaw, Poland

   Contribution

   Investigation, Writing – original draft

   Competing interests

   No competing interests declared

7. Lukasz Bozycki

   1. School of Pharmacy and Biomedical Sciences, University of Portsmouth, Portsmouth, United Kingdom
   2. Laboratory of Cellular Metabolism, Nencki Institute of Experimental Biology, Warsaw, Poland

   Contribution

   Investigation, Visualization

   Competing interests

   No competing interests declared

8. Michal Korostynski

   Laboratory of Pharmacogenomics, Maj Institute of Pharmacology PAS, Krakow, Poland

   Contribution

   Software, Formal analysis, Supervision, Writing – original draft, Writing – review and editing

   Competing interests

   No competing interests declared

9. Samuel C Robson

   1. School of Pharmacy and Biomedical Sciences, University of Portsmouth, Portsmouth, United Kingdom
   2. Centre for Enzyme Innovation, University of Portsmouth, Portsmouth, United Kingdom

   Contribution

   Data curation, Formal analysis, Supervision, Investigation, Writing – review and editing

   Competing interests

   SR received royalties from Cancer Research Technology Ltd through University of Cambridge Enterprise Ltd under agreement A13086 for "Chromatin-bound METTL3 m6A methyltransferase facilitates myeloid leukaemogenesis by modulating ribosome stalling"

   "This ORCID iD identifies the author of this article:" 0000-0001-5702-9160

10. Claudio Angione

    School of Computing, Engineering and Digital Technologies, Teesside University, Middlesbrough, United Kingdom

    Contribution

    Conceptualization, Software, Formal analysis, Supervision, Methodology

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0002-3140-7909

11. Christian Pinset

    CNRS, I-STEM, AFM, Corbeil-Essonnes, France

    Contribution

    Formal analysis, Supervision, Writing – review and editing

    Competing interests

    No competing interests declared

12. Dariusz C Gorecki

    School of Pharmacy and Biomedical Sciences, University of Portsmouth, Portsmouth, United Kingdom

    Contribution

    Conceptualization, Supervision, Funding acquisition, Investigation, Writing – original draft, Project administration, Writing – review and editing

    For correspondence

    darek.gorecki@port.ac.uk

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0003-3584-1654

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