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Circulating Biomarkers for Duchenne Muscular Dystrophy

Abstract

Duchenne muscular dystrophy is the most common form of muscular dystrophy. Genetic and biochemical research over the years has characterized the cause, pathophysiology and development of the disease providing several potential therapeutic targets and/or biomarkers. High throughput – omic technologies have provided a comprehensive understanding of the changes occurring in dystrophic muscles. Murine and canine animal models have been a valuable source to profile muscles and body fluids, thus providing candidate biomarkers that can be evaluated in patients. This review will illustrate known circulating biomarkers that could track disease progression and response to therapy in patients affected by Duchenne muscular dystrophy. We present an overview of the transcriptomic, proteomic, metabolomics and lipidomic biomarkers described in literature. We show how studies in muscle tissue have led to the identification of serum and urine biomarkers and we highlight the importance of evaluating biomarkers as possible surrogate endpoints to facilitate regulatory processes for new medicinal products.

Duchenne muscular dystrophy (DMD) is a severe muscle-wasting disease caused by genetic mutations in the DMD gene encoding a structural protein called dystrophin [1]. Mutations in the same gene are responsible for the milder form of the disease, which is called Becker muscular dystrophy (BMD) [2]. Protein truncating mutations cause the Duchenne form of the disease characterized by complete or almost complete absence of dystrophin, while BMD patients have in frame mutations and are partly protected from muscular degeneration by reduced levels of (smaller or semi-functional) dystrophins [3, 4]. During the years following the gene discovery, research focus has been on the development of potential therapies able to restore dystrophin and the dystrophin associated glycoprotein complex (dystrophin pre-mRNA splicing modulation with antisense oligonucleotides, dystrophin mRNA ribosomal read-through of non-sense mutations, gene therapy, allogenic or genetically corrected autologous stem cells, utrophin up-regulation and differential glycosylation of α-dystroglycan) or to reduce the secondary pathology caused by the absence of dystrophin (reducing oxidative stress or increasing muscle mass) [5–16]. These therapeutic strategies were optimized and proof of concept was shown in cellular and animal models [17–30]. In fact the development of therapeutic strategies was so fast that when the first clinical trials were designed it was clear which mutation specific drug was suitable for which patients, while it was not known which primary endpoint should be used. Furthermore, lacking detailed knowledge on the natural history of the disease and the outcome measures to be used in clinical trials, it was difficult to power studies properly and to identify biomarkers for therapeutic monitoring. Finally, while detailed knowledge was available about general genotype-phenotype relationships, studies linking different out-of-frame mutations to variation observed in natural history studies using outcome measures used in clinical trials were unavailable, nor was it known if and how genetic modifiers influenced disease progression as measured by these functional outcome measures [31]. Once the field realized this, clinical researchers started to evaluate how known and new functional scales could describe disease progression in patients with Duchenne [31–41]. These studies in natural history cohorts provided a baseline for clinical trials, but meanwhile clinical trials had already initiated. On occasion this made the interpretation of results difficult as natural history studies showed that individual differences in disease progression were due to genetic modifiers or differences in the mutation site [42–47]. Importantly, natural history studies also revealed that the progression of the disease over time was different depending on the age group and the walking skills at baseline as measured by the 6 minute walk test (6MWT) [48]. Molecular researchers in parallel continued to investigate the pathophysiological changes in patients’ muscle biopsies and animal models by proteomic and gene expression studies. Even though studies in muscle biopsies have limitations such as the fact that they do not reflect the condition of the entire muscle nor of other muscles, this approach increased the understanding of which genes and proteins were driving the pathology in Duchenne as well as in other muscular dystrophies, showing common characteristics that could lead to a dystrophic phenotype [49]. Some of these genes were then identified as modifiers of disease progression or prognostic biomarkers [44]. We will however not discuss genetic modifiers as they have been recently reviewed by Lamar et al. in the first issue of this Journal [50]. The identification of muscle biomarkers lead the way to the identification of new therapeutic targets as well as biomarkers which could be used as surrogate endpoints or secondary endpoints. Even though the categorization of biomarkers is beyond the scope of this review we would like to spend a few words on the term surrogate endpoint to avoid confusion. The term “biomarker” has been defined by the Biomarkers Definitions Working Group of the National Institutes of Health (NIH) as“a characteristic that is objectively measured and evaluated as an indicator of normal biological processes, pathogenic processes, or pharmacologic response to a therapeutic intervention” [51]. This definition is quite ample and may include many types of possible biomarkers such as prognostic biomarkers, diagnostic biomarkers and biomarkers for response to a therapeutic agent. A surrogate endpoint is a “biomarker that is intended to substitute a clinical endpoint. A surrogate endpoint is expected to predict clinical benefit (or harm or lack of benefit or harm) based on epidemiologic, therapeutic, pathophysiologic, or other scientific evidence”. Based on this definition it is clear that all surrogate endpoints are biomarkers but not all biomarkers can be surrogate endpoints. To qualify as surrogate endpoint a biomarker should correlate to, or predict clinical endpoints. As such, it is not sufficient to know that the levels of the biomarker differ between DMD patients and age-matched controls. Rather, natural history data of the biomarker should be available as well as information on how the biomarker levels correlate with disease progression and functional endpoints. Excellent papers further explaining the characteristics that a biomarker should have to classify as surrogate endpoint are available and we would like to refer the reader to these papers [52–54]. For DMD patients, dystrophin restoration is an obvious pharmacodynamic biomarker for DMD therapies aiming at dystrophin restoration; dystrophin analysis has been covered in depth in a review paper in the inaugural issue of this Journal [55] and will therefore not be discussed here.

As it frequently happens in science, the identification of candidate targets (in this case biomarkers that could be suitable surrogate endpoints) is based on the availability of enabling technologies. Several complementary approaches have been used to identify nucleic acids, proteins, peptides, metabolites and lipids that are able to discriminate between DMD patients and healthy controls. Discovery has been mostly driven by non-targeted high-throughput technologies even though several groups have identified biomarkers based on a priori hypotheses. We will here describe reported candidate biomarkers based on their chemical nature.

NUCLEIC ACIDS

Thanks to the development of micro-arrays first and next generation sequencing later, gene expression studies have been one of the major determinants for understanding the pathophysiological changes in Duchenne patients’ cell lines and muscles as well as in animal models [56–63]. The genes identified in several independent studies belong to pathways involved in energy production, muscle regeneration and contraction, inflammation, calcium homeostasis, fibrosis and macrophage infiltration. The TGF-β pathway has been central in many reports and recently it was shown that correct phasing of TGF-β signalling is crucial for successful muscle regeneration. Some genes belonging to the TGF-β and IGF-1 pathways have been considered as therapeutic targets for DMD (e.g. myostatin and Akt), others have led to the discovery of genetic modifiers (e.g. SPP1 gene) and to the identification of elevated gene products in circulation (e.g. MMP-9). Interestingly some genes such as Cd68 (macrophages marker), Lgals3 and Bgn showed normalization towards control levels upon dystrophin restoration in the mdx mouse, thus qualifying as candidates biomarkers for the evaluation of therapeutic treatment [64]. It is important to mention that Cd68 and Lgals3 also correlated with disease severity at 6 weeks of age in 3 dystrophinopathy mouse models with different levels of utrophin expression [65]. Other studies identified differential expression of miRNAs in muscles of animal models and patients [66–70]. Among the most reported miRNAs are miR-206 (linked to muscle regeneration in DMD and in patients affected by amyotrophic lateral sclerosis [71]), miR-1 and miR-133 (highly expressed in skeletal muscle), miR-29c (linked to fibrosis), miR-31 (targeting dystrophin), miR-378 (myofiber enriched miRNA), miR-499 and miR-208 (cardiac enriched miRNAs). Multiple research groups investigated the potential of these and other miRNAs to act as peripheral biomarkers and they evaluated the presence of these miRNAs in sera of animal models and patients [72–76]. While dystromiRs remain a good tool to differentiate between cases and controls, not only for DMD but also for other muscular dystrophies [68, 73], the initial correlation with disease severity [72] has not been confirmed in a larger study [77].

PROTEINS AND PEPTIDES

Creatine kinase (CK) is an enzyme that is abundant in muscle, which leaks into the bloodstream upon muscle damage. As such serum CK activity has been used to diagnose muscle damage and muscular dystrophy for more than 50 years and for this special issue dedicated to the launch of the John Walton Muscular Dystrophy Research Centre we would like to cite a paper that underlines the contribution of Lord Walton to these findings, which paved the way to biomarker discoveries in Duchenne over time [78]. Serum CK activity has been extensively studied and even though CK activity in serum has several limitations (such as seasonal variation [79] and intra/inter individual variability [80]), it remains still one of the first evidences that could lead to a diagnosis of muscular dystrophy. Reports in literature showed however that CK activity is mainly useful as a diagnostic biomarker as it peaks between 1 and 6 years of age and decreases with age as the disease progresses [81]. This decrease reflects the replacement of muscle tissue by fibrotic and adipose tissues. Therefore, CK activity is of little use for therapeutic monitoring, since lower CK levels can mean both that the disease progressed further (muscle quality further decreased) or that the muscle quality improved (less leakage of CK). As for gene expression studies, researchers initially focused their attention on muscle tissue, with the intention to better understand the pathophysiology and identify therapeutic targets and biomarker candidates. Proteomic studies in the mdx mice, especially in the diaphragm muscle, have provided a number of candidate proteins that are elevated or reduced in dystrophic muscles. Results showed alteration in nucleotide metabolism, luminal and cytosolic calcium handling, glycolytic enzymes, mitochondrial energy metabolism, oxidative stress, cytoskeletal proteins and proteins present in the extracellular matrix [82–87]. Experiments in aged mdx mice provided further evidence that proteins of the extracellular matrix are elevated with age and that myofibrillar proteins decrease with age [88, 89]. Further comparison of heart muscle tissue between young and old mdx mice helped to distinguish between differences due to aging and differences due to dystrophin deficiency, highlighting again impaired mitochondrial metabolism, contractile function and cell signalling [90]. More studies in muscle of mdx-4cv mice showed an increase in extracellular matrix and cytoskeletal proteins and a reduction in contractile proteins [91]. Similar findings have been obtained in the canine model of muscular dystrophy [92]. Interestingly studies of muscles that are relatively spared in Duchenne patients and animal models, such as extra-ocular and sartorius muscles, provided potential evidence that the dystrophin paralog utrophin and muscle hypertrophy could exert a protective effect on affected muscles [93, 94]. Several studies exist where the obtained knowledge was used to propose therapeutic targets, but less effort has been put in trying to translate the muscle findings to serum/plasma biomarkers. Studying the serum proteome is still an analytical challenge even with the newest technologies. The high dynamic range and the presence of high abundant proteins has so far reduced the successful identification of serum biomarkers using mass spectrometry based approaches. Nevertheless researchers have been able to identify and replicate associations for a number of proteins in the recent years using unbiased approaches such as mass spectrometry and targeted approaches such as antibody and aptamers based assays. Alagaratnam et al. identified a peptide belonging to factor XIII as serum discriminator between mdx and wild type mice [95]; Colussi et al. reported increased fibrinogen and glutathione peroxidase (GPX3) and reduced levels of gelsolin and leukemia inhibitory factor receptor (LIFr) [96]. Notably, GPX3 and LIFr normalized after treatment with a histone deacetylase inhibitor, suggesting these biomarkers might be used to monitor therapy. Fibrinogen was also found in two other studies [97, 98] and it has been linked to the formation of fibrotic tissue in mice [99]. Nadarajah et al. reported serum MMP-9 levels to be elevated in DMD patients and to increase with age [100]. This finding was later on confirmed by an independent group [98]. Martin et al. found elevated levels of fibronectin in DMD sera [101]. Ayoglu and colleagues performed a comprehensive study using a customized antibody array where hundreds of proteins were quantified in sera and plasma of Duchenne and Becker patients [49]. The most interesting candidates were carbonic anhydrase 3, myosin light chain 3, malate dehydrogenase 2, electron transfer flavoprotein subunit alpha (mitochondrial) and beta and troponin T. Hathout and co-workers identified other glycolytic enzymes such as glycogen phosphorylase and fructose-bisphosphate aldolase A, myofibrillar proteins such as myomesin-3 and titin [98]. Titin has also been found in DMD patients’ urine [102]. Recently an aptamer-based study quantified 1125 proteins in serum of DMD and healthy controls and replicated the findings in an independent cohort: 44 proteins were found to be differentially represented between DMD patients and controls [103]. The authors classified the proteins into 4 groups among which the muscle derived proteins show a CK like behaviour (elevated compared to controls, but down-trending with age). These results show that the muscle degeneration processes ongoing in patients’ muscles are indeed reflected in the circulation. Notably the other 3 groups showed other characteristics that are more interesting from a surrogate endpoint perspective. They contained markers that are elevated or decreased at all ages, or markers for which the levels are comparable between young patients and controls, but levels change in opposite direction with age (and presumably resemble disease severity). Most of the markers in these groups were not enriched in muscle. Since the patterns vary between DMD and control individuals of different ages, they may have the potential to monitor treatment effect in clinical trials. However, longitudinal studies in individual patients are needed to elucidate the natural history of these markers and to assess whether these markers are indeed candidate biomarkers to act as surrogate endpoints, i.e. whether their levels correlate or anticipate functional outcome measures used in clinical trials. Notably, the co-linearity between age and disease progression (assessed by e.g. 6MWT) may make it difficult to interpret changes as age remains one of the best predictors of disease progression. Finally, studies are needed to understand how biomarkers levels respond to therapeutic intervention, e.g. by analysing serum samples of DMD patients participating to clinical trials. This process is laborious and therefore in our opinion should focus on candidate biomarkers that have been reported to be elevated compared to healthy controls and rapidly increase with age or the ones that are reduced compared to controls and rapidly decreasing with age. Some examples of these are Metalloproteinase-9, ETFA/ETFB, Adiponectin, Persephin, Prolyl endopeptidase FAP, Osteomodulin, Proto–oncogene tyrosine–protein kinase receptor Ret, Complement decay–accelerating factor, Growth/differentiation factor 11, Gelsolin and Tumor necrosis factor receptor superfamily member 19L [98, 100, 103].

METABOLITES AND LIPIDS

There is less evidence in literature of a muscular metabolic signature in circulation in patients with DMD. However early evidence was published in 1984 by Shapira and colleagues that a vitamin D metabolite (24,25(OH)2D3) was less abundant in DMD patients’ sera compared to healthy controls [104]. The authors stressed in the article how the findings were probably related to muscle ATP and calcium homeostasis and in those years the link between calcium metabolism, muscle contraction and vitamin D was suggested [105]. It took fifteen years before other groups could study in more detail metabolic perturbations in vivo since in those days animal models for DMD were barely available (the mdx mouse had just been published [106]). McIntosh et al. described the association between taurine levels in muscle and muscle regeneration [107]. Griffin et al. obtained metabolic profiles for skeletal muscle, heart, cortex and cerebellum in dystrophic mice and identified taurine and creatine as strong classifiers [108, 109]. The same group showed that utrophin could partially restore the metabolic signature in diaphragm of dystrophic mice, showing the potential of metabolic data to act as biomarker for therapeutic treatment [110]. Franciotta and colleagues tested whether creatinine concentration in 24 hours urine could be used as an indirect measure of skeletal muscle mass in DMD patients but they could not find a significant association [111]. Recently a large study reported that a prostaglandin D2 metabolite was elevated in the urine of DMD patients over controls and that the concentration further increased in patients above 8 years of age, making it a good candidate biomarker also for patients who are in the declining ambulatory phase [112]. In 2010 it was reported that DMD patients have higher serum concentrations of triglycerides, phospholipids, free cholesterol, cholesterol esters and total cholesterol compared to healthy controls and that the ratio between phospholipids and cholesterol had the highest discriminant power [113]. Very recently Hörster and colleagues reported that the L-arginine/nitric oxide pathway regulating endothelial function is affected in DMD patients urine and plasma and that treatment with corticosteroids reduced the intensity of this signature [114].

WHAT IS MISSING

Very little effort has been put in the integration of the many datasets available. There are only a couple of examples in literature where metabolomic and proteomic data have been combined and in these studies taurine could be linked to oxidative phosphorylation and mitochondrial metabolism [115, 116]. More effort should be put in the integration of datasets to have a complete understanding of the pathophysiology and to identify molecular targets which can serve as biomarkers [117]. Evidence from other fields shows how important it is to integrate datasets to have a good understanding of the biology, plan interventions and facilitate therapy [118]. Datasets should be available in public repositories and bioinformatic tools should be developed to enable the comparison of different datasets, for example at the pathway level. The integration of different datasets on such a higher level would reduce the number of pathways, ontologies or concepts to be tested thus reducing the number of tests and increasing power to identify significant pathways and molecules that can serve as therapeutic targets or biomarkers. This is particularly important for rare diseases such as DMD because of the low numbers of samples available. Once pathways and molecular candidates are identified, they should be replicated in large cohorts to identify biomarkers that can predict or correlate to clinical endpoints. The association of a biomarker with clinical endpoints is a key point to enable the translation of a candidate biomarker into a surrogate endpoint. To date there are no surrogate endpoints for DMD but natural history studies and placebo arms of clinical trials represent a unique opportunity because longitudinal samples are available and biomarker data could be associated with known and newly developed clinical endpoints. These and other studies should also try to understand how confounders such as age, progression, biological and environmental co-factors and physical activity affect the robustness, sensitivity and specificity of individual biomarkers. The analysis of biomarkers in trials for corticosteroid use (such as the FOR-DMD trial - http://for-dmd.org) would also enable to quantify the effect of different steroid regimens on biomarker levels. Furthermore, when biomarkers respond to steroid treatment, this is important knowledge because most of the current therapies are tested in trials on top of prednisone or deflazacort treatment. Biomarkers should be also linked to specific aspects of the disease such as the amount of fibrofatty infiltration in muscle and cardiac function. These aspects can be evaluated by magnetic resonance imaging, which in itself is also a promising candidate biomarker for DMD [119, 120]. The possibility to closely monitor heart function with circulating biomarkers is needed not only for DMD patients, but also in Becker patients where severity of skeletal muscle involvement is not a predictor of cardiac involvement. Some candidates have already been identified with the identification of cardiac specific dystrophin binders [121]. Last but not least a link between dystrophin levels (in case of dystrophin restoring drugs) and clinical outcome needs to be established to make dystrophin not only a pharmacodynamic biomarker but also a surrogate endpoint. So far this association has only been studied in Becker patients and in patients with an exclusive heart involvement [122, 123]. The high variability in functional performance and the wide age range of Becker patients combined with the small sample size have so far hampered a complete understanding of this relation [55]. For other drugs (e.g utrophin up-regulation), the target levels (utrophin or the dystrophin associated glycoprotein complex levels) should be reliably quantified to consider these readouts as pharmacodynamic biomarkers. The available assays were not set up to consistently quantify these outcomes (e.g. distribution and quantification of utrophin along the myofibers membrane) since standards of quantification are not available and the available assays have been mainly designed for research purposes. Standard operating procedures should be developed and the quantified levels should be connected to clinical performance to consider the readouts surrogate endpoints.

CONCLUSIONS

Enabling technologies have driven the discovery of deregulated pathways in DMD. These studies have produced lists of candidate therapeutic targets and biomarkers. During the last 5 years many candidates have been evaluated for their potential to serve as non-invasive biomarkers by measuring their concentration in body fluids. The potential of these candidates as surrogate endpoints needs to be evaluated in ad hoc studies where molecular and clinical outcomes can be compared. The availability of surrogate endpoints has the potential to facilitate regulatory approval of medicinal compounds for patients affected by Duchenne muscular dystrophy.

CONFLICT OF INTEREST

Pietro Spitali and Annemieke Aartsma-Rus declare being employed by the LUMC and receiving salary from the LUMC. LUMC has patents on exon skipping, some of which Dr. Aartsma-Rus is co-inventor on. On sublicensing some of these patents to Prosensa Therapeutics and GSK, Dr. Aartsma-Rus has received a share of royalty payments from LUMC.

ACKNOWLEDGMENTS

Authors would like to acknowledge the Association Française contre les Myopathies (AFM) (grants 15092 and 17724) and the FP7 funded Neuromics project (grant number 305121) for supporting the ongoing biomarker work on muscular dystrophies at the Leiden University Medical Center. Authors would like to acknowledge Dr. Willeke van Roon-Mom and the reviewers for critical reading of the manuscript.

REFERENCES

1 

Hoffman EP, Brown RHJr, Kunkel LM1987Dystrophin: The protein product of the Duchenne muscular dystrophy locusCell516919928

2 

Monaco AP1989Dystrophin, the protein product of the Duchenne/Becker muscular dystrophy geneTrends Biochem Sci1410412415

3 

Koenig M, Beggs AH, Moyer M1989The molecular basis for Duchenne versus Becker muscular dystrophy:Correlation of severity with type of deletionAm J Hum Genet454498506

4 

Flanigan KM, Dunn DM, von NA2011Nonsense mutation-associated Becker muscular dystrophy: Interplay betweenexon definition and splicing regulatory elements within the DMD geneHum Mutat323299308

5 

Aartsma-Rus A, Muntoni F2013194th ENMC international worksho3rd ENMC workshop on exon skipping: Towards clinical application of antisense-mediated exon skipping for Duchenne muscular dystrophy 8-10 December Naarden, The NetherlandsNeuromuscul Disord2311934944

6 

Haas M, Vlcek V, Balabanov P2015European Medicines Agency review of ataluren for the treatment of ambulantpatients aged 5 years and older with Duchenne muscular dystrophy resulting from a nonsense mutation in thedystrophin geneNeuromuscul Disord251513

7 

Mendell JR, Campbell K, Rodino-Klapac L2010Dystrophin immunity in Duchenne’s muscular dystrophyN Engl JMed3631514291437

8 

Noviello M, Tedesco FS, Bondanza A2014Inflammation converts human mesoangioblasts into targets ofalloreactive immune responses: Implications for allogeneic cell therapy of DMDMol Ther22713421352

9 

Dorchies OM, Wagner S, Buetler TM2009Protection of dystrophic muscle cells with polyphenols from green teacorrelates with improved glutathione balance and increased expression of 67LR, a receptor for(-)-epigallocatechin gallateBiofactors353279294

10 

Yang SY, Hoy M, Fuller B2010Pretreatment with insulin-like growth factor I protects skeletal muscle cellsagainst oxidative damage via PI3K/Akt and ERK1/2 MAPK pathwaysLab Invest903391401

11 

Bish LT, Sleeper MM, Forbes SC2011Long-term systemic myostatin inhibition via liver-targeted gene transferin Golden Retriever Muscular DystrophyHum Gene Ther221214991509

12 

Bushby K, Finkel R, Wong B2014Ataluren treatment of patients with nonsense mutation dystrophinopathyMuscleNerve504477487

13 

Li HL, Fujimoto N, Sasakawa N2015Precise correction of the dystrophin gene in duchenne muscular dystrophypatient induced pluripotent stem cells by TALEN and CRISPR-Cas9Stem Cell Reports41143154

14 

Martin PT, Xu R, Rodino-Klapac LR2009Overexpression of Galgt2 in skeletal muscle prevents injury resultingfrom eccentric contractions in both mdx and wild-type miceAm J Physiol Cell Physiol2963C476C488

15 

Yoon JH, Johnson E, Xu R2012Comparative proteomic profiling of dystroglycan-associated proteins in wildtype, mdx, and Galgt2 transgenic mouse skeletal muscleJ Proteome Res11944134424

16 

Guiraud S, Squire SE, Edwards B2015Second-generation compound for the modulation of utrophin in the therapyof DMDHum Mol GenetMay 1. pii: ddv154. [Epub ahead of print]

17 

Heemskerk H, de WC, van KP2010Preclinical PK and PD studies on 2’-O-methyl-phosphorothioate RNA antisenseoligonucleotides in the mdx mouse modelMol Ther18612101217

18 

Heemskerk HA, de Winter CL, de Kimpe SJ2009In vivo comparison of 2’-O-methyl phosphorothioate andmorpholino antisense oligonucleotides for Duchenne muscular dystrophy exon skippingJ Gene Med113257266

19 

Malerba A, Boldrin L, Dickson G2011Long-term systemic administration of unconjugated morpholino oligomers for therapeutic expression of dystrophin by exon skipping in skeletal muscle: Implications for cardiac muscle integrityNucleic Acid Ther214293298

20 

Malerba A, Sharp PS, Graham IR2011Chronic systemic therapy with low-dose morpholino oligomers amelioratesthe pathology and normalizes locomotor behavior in mdx miceMol Ther192345354

21 

Yin H, Saleh AF, Betts C2011Pip5 transduction peptides direct high efficiency oligonucleotide-mediateddystrophin exon skipping in heart and phenotypic correction in mdx miceMol Ther19712951303

22 

Kayali R, Ku JM, Khitrov G2012Read-through compound 13restores dystrophin expression and improves muscle function in themdx mouse model for Duchenne muscular dystrophyHum Mol Genet211840074020

23 

Gregorevic P, Blankinship MJ, Allen JM2008Systemic microdystrophin gene delivery improves skeletal musclestructure and function in old dystrophic mdx miceMol Ther164657664

24 

Goyenvalle A, Vulin A, Fougerousse F2004Rescue of dystrophic muscle through U7 snRNA-mediated exon skippingScience306570217961799

25 

Sampaolesi M, Blot S, D’Antona G2006Mesoangioblast stem cells ameliorate muscle function in dystrophic dogsNature4447119574579

26 

Morine KJ, Bish LT, Selsby JT2010Activin IIB receptor blockade attenuates dystrophic pathology in a mousemodel of Duchenne muscular dystrophyMuscle Nerve425722730

27 

Morine KJ, Bish LT, Pendrak K2010Systemic myostatin inhibition via liver-targeted gene transfer in normaland dystrophic micePLoS One52e9176

28 

Dumonceaux J, Marie S, Beley C2010Combination of myostatin pathway interference and dystrophin rescueenhances tetanic and specific force in dystrophic mdx miceMol Ther185881887

29 

Qiao C, Li J, Jiang J2008Myostatin propeptide gene delivery by adeno-associated virus serotype 8 vectorsenhances muscle growth and ameliorates dystrophic phenotypes in mdx miceHum Gene Ther193241254

30 

Wagner KR, McPherron AC, Winik N2002Loss of myostatin attenuates severity of muscular dystrophy in mdx miceAnn Neurol526832836

31 

Lynn S, Aartsma-Rus A, Bushby K2015Measuring clinical effectiveness of medicinal products for the treatmentof Duchenne muscular dystrophyNeuromuscul Disord25196105

32 

Mayhew A, Mazzone ES, Eagle M2013Development of thePerformance of the Upper Limb module for Duchenne musculardystrophyDev Med Child Neurol551110381045

33 

McDonald CM, Henricson EK, Han JJ2010The 6-minute walk test as a new outcome measure in Duchenne musculardystrophyMuscle Nerve414500510

34 

Mazzone ES, Pane M, Sormani MP201324 month longitudinal data in ambulant boys with duchenne musculardystrophyPLoS One81e52512

35 

Ricotti V, Ridout DA, Pane M2015The NorthStar AmbulatoryAssessment in Duchenne muscular dystrophy: Considerations for thedesign of clinical trialsJ NeurolNeurosurg Psychiatry10.1136/jnnp-2014-309405Mar 2. pii: jnnp-2014-309405. [Epub ahead of print]

36 

Pane M, Mazzone ES, Sivo S2014The 6 minute walk testand performance of upper limb in ambulant duchennemuscular dystrophy boysPLoS Curr6

37 

De SR, Pane M, Sivo S2015Suitability of North Star Ambulatory Assessment in young boys with Duchennemuscular dystrophyNeuromuscul Disord2511418

38 

Orcesi S, Ariaudo G, Mercuri E2014A new self-report quality of life questionnaire for children withneuromuscular disorders: Presentation of the instrument, rationale for its development, and some preliminaryresultsJ Child Neurol292167181

39 

McDonald CM, Henricson EK, Abresch RT2013The 6-minute walk test and other endpoints in Duchenne musculardystrophy: Longitudinal natural history observations over 48 weeks from a multicenter studyMuscle Nerve483343356

40 

McDonald CM, Henricson EK, Abresch RT2013The 6-minute walk test and other clinical endpoints in duchennemuscular dystrophy: Reliability, concurrent validity, and minimal clinically important differences from amulticenter studyMuscle Nerve483357368

41 

Henricson E, Abresch R, Han JJ2012Percent-predicted 6-minute walk distance in duchenne muscular dystrophy toaccount for maturational influencesPLoS Curr4RRN1297

42 

Bello L, Piva L, Barp A2012Importance of SPP1 genotype as a covariate in clinical trials in Duchennemuscular dystrophyNeurology792159162

43 

Bello L, Kesari A, Gordish-Dressman H2015Genetic modifiers of ambulation in the cooperative internationalNeuromuscular research group Duchenne natural history studyAnn Neurol774684696

44 

Pegoraro E, Hoffman EP, Piva L2011SPP1 genotype is a determinant of disease severity in Duchenne musculardystrophyNeurology763219226

45 

Flanigan KM, Ceco E, Lamar KM2012LTBP4 genotype predicts age of ambulatory loss in duchenne musculardystrophyAnn Neurol764481488

46 

van den Bergen JC, Hiller M, Bohringer S2014Validation ofgenetic modifiers for Duchenne muscular dystrophy: A multicentrestudy assessing SPP1 and LTBP4 variantsJ Neurol NeurosurgPsychiatry10.1136/jnnp-2014-3084092014 Dec 4. pii: jnnp-2014-308409. [Epub ahead of print]

47 

Anthony K, Arechavala-Gomeza V, Ricotti V2014Biochemical characterization of patients with in-frame orout-of-frame DMD deletions pertinent to exon 44 or 45 skippingJAMA Neurol7113240

48 

Pane M, Mazzone ES, Sivo S2014Long term natural history datain ambulant boys with Duchenne musculardystrophy: 36-monthchangesPLoS One910e108205

49 

Ayoglu B, Chaouch A, Lochmuller H2014Affinity proteomics within rare diseases: A BIO-NMD study for bloodbiomarkers of muscular dystrophiesEMBO Mol Med67918936

50 

Kay-Marie Lamar, McNally Elizabeth M2015Genetic Modifiers for Neuromuscular DiseasesJournal of Neuromuscular Diseases11313

51 

2001Biomarkers and surrogate endpoints: Preferred definitions and conceptual frameworkClin Pharmacol Ther6938995

52 

Aronson JK2005Biomarkers and surrogate endpointsBr J Clin Pharmacol595491494

53 

Aronson JK2012Research priorities in biomarkers and surrogate end-pointsBr J Clin Pharmacol736900907

54 

Scotton C, Passarelli C, Neri M2014Biomarkers in rare neuromuscular diseasesExp Cell Res32514449

55 

Aartsma-Rus A2015Dystrophin analysis in clinical trialsJournal of Neuromuscular Diseases114153

56 

Pescatori M, Broccolini A, Minetti C2007Gene expression profiling in the early phases of DMD: A constantmolecular signature characterizes DMD muscle from early postnatal life throughout disease progressionFASEB J21412101226

57 

Haslett JN, Sanoudou D, Kho AT2002Gene expression comparison of biopsies from Duchenne muscular dystrophy(DMD) and normal skeletal muscleProc Natl Acad Sci U S A99231500015005

58 

Haslett JN, Sanoudou D, Kho AT2003Gene expression profiling of Duchenne muscular dystrophy skeletal muscleNeurogenetics44163171

59 

Bakay M, Zhao P, Chen J2002A web-accessible complete transcriptome of normal human and DMD muscleNeuromuscul Disord12 Suppl 1S125S141

60 

Chen YW, Zhao P, Borup R2000Expression profiling in the muscular dystrophies: Identification of novelaspects of molecular pathophysiologyJ Cell Biol151613211336

61 

Turk R, Sterrenburg E, de Meijer EJ2005Muscle regeneration in dystrophin-deficient mdx mice studied by geneexpression profilingBMC Genomics698

62 

Bakay M, Chen YW, Borup R2002Sources of variability and effect of experimental approach on expressionprofiling data interpretationBMC Bioinformatics34

63 

Kotelnikova E, Shkrob MA, Pyatnitskiy MA2012Novel approach to meta-analysis of microarray datasets revealsmuscle remodeling-related drug targets and biomarkers in Duchenne muscular dystrophyPLoS Comput Biol82e1002365

64 

’t Hoen PA, van der Wees CG, Aartsma-Rus A2006Gene expression profiling to monitor therapeutic and adverseeffects of antisense therapies for Duchenne muscular dystrophyPharmacogenomics73281297

65 

van Putten M, Kumar D, Hulsker M2012Comparison of skeletalmuscle pathology and motor function of dystrophin and utrophindeficient mouse strainsNeuromuscul Disord225406417

66 

Liu N, Williams AH, Maxeiner JM2012microRNA-206 promotes skeletal muscle regeneration and delays progressionof Duchenne muscular dystrophy in miceJ Clin Invest122620542065

67 

Cacchiarelli D, Incitti T, Martone J2011miR-31 modulates dystrophin expression: New implications forDuchenne muscular dystrophy therapyEMBO Re122136141

68 

Eisenberg I, Eran A, Nishino I2007Distinctive patterns of microRNA expression in primary muscular disordersProc Natl Acad Sci U S A104431701617021

69 

Greco S, De SM, Colussi C2009Common micro-RNA signature in skeletal muscle damage and regeneration inducedby Duchenne muscular dystrophy and acute ischemiaFASEB J231033353346

70 

Cacchiarelli D, Martone J, Girardi E2010MicroRNAs involved inmolecular circuitries relevant for the Duchenne muscular dystrophypathogenesis are controlled by the dystrophin/nNOS pathwayCellMetab124341351

71 

Williams AH, Valdez G, Moresi V2009MicroRNA-206 delays ALS progression and promotes regeneration of neuromuscular synapses in miceScience326595915491554

72 

Cacchiarelli D, Legnini I, Martone J2011miRNAs as serum biomarkers for Duchenne muscular dystrophyEMBO MolMed35258265

73 

Vignier N, Amor F, Fogel P2013Distinctive serum miRNA profile in mouse models of striated muscularpathologiesPLoS One82e55281

74 

Jeanson-Leh L, Lameth J, Krimi S2014Serum profiling identifies novel muscle miRNA and cardiomyopathy-relatedmiRNA biomarkers in Golden Retriever muscular dystrophy dogs and Duchenne muscular dystrophy patientsAm JPathol1841128852898

75 

Roberts TC, Godfrey C, McClorey G2013Extracellular microRNAs are dynamic non-vesicular biomarkers of muscleturnoverNucleic Acids Res412095009513

76 

Roberts TC, Blomberg KE, McClorey G2012Expression analysis in multiple muscle groups and serum revealscomplexity in the microRNA transcriptome of the mdx mouse with implications for therapyMol Ther Nucleic Acids1e39

77 

Zaharieva IT, Calissano M, Scoto M2013Dystromirsasserum biomarkers for monitoring the disease severityinDuchenne muscular DystrophyPLoS One811e80263

78 

Pearce JM, Pennington RJ, Walton JN1964Serum enzyme studiesin muscle disease. III. Serum creatine kinase activity inrelatives of patients with the Duchenne type of musculardystrophyJ Neurol Neurosurg Psychiatry27181185

79 

Percy ME, Andrews DF, Thompson MW1982Serum creatine kinase in the detection of Duchenne muscular dystrophy carriers: Effects of season and multiple testingMuscle Nerve515864

80 

Nicholson GA, Morgan G, Meerkin M1985The creatine kinasereference interval. An assessment of intra- and inter-individualvariationJ Neurol Sci712-3225231

81 

Zatz M, Rapaport D, Vainzof M1991Serum creatine-kinase (CK) and pyruvate-kinase (PK) activities in Duchenne(DMD) as compared with Becker (BMD) muscular dystrophyJ Neurol Sci1022190196

82 

Ge Y, Molloy MP, Chamberlain JS2003Proteomic analysis of mdx skeletal muscle: Great reduction of adenylatekinase 1 expression and enzymatic activityProteomics31018951903

83 

Doran P, Dowling P, Lohan J2004Subproteomics analysis of Ca+-binding proteins demonstrates decreasedcalsequestrin expression in dystrophic mouse skeletal muscleEur J Biochem2711939433952

84 

Doran P, Martin G, Dowling P2006Proteome analysis of the dystrophin-deficient MDX diaphragm reveals adrastic increase in the heat shock protein cvHSPProteomics61646104621

85 

Gardan-Salmon D, Dixon JM, Lonergan SM2011Proteomicassessment of the acute phase of dystrophindeficiency inmdx miceEur J Appl Physiol1111127632773

86 

Rayavarapu S, Coley W, Cakir E2013Identification of diseasespecific pathways using in vivo SILAC proteomics indystrophin deficient mdx mouseMol Cell Proteomics12510611073

87 

Ramadasan-Nair R, Gayathri N, Mishra S2014Mitochondrial alterations and oxidative stress in an acutetransient mouse model of muscle degeneration: Implications for muscular dystrophy and related muscle pathologiesJ Biol Chem2891485509

88 

Carberry S, Zweyer M, Swandulla D2012Profiling of age-related changes in the tibialis anterior muscleproteome of the mdx mouse model of dystrophinopathyJ Biomed Biotechnol691641

89 

Carberry S, Zweyer M, Swandulla D2012Proteomics reveals drastic increase of extracellular matrix proteinscollagen and dermatopontin in the aged mdx diaphragm model of Duchenne muscular dystrophyInt J Mol Med302229234

90 

Holland A, Dowling P, Zweyer M2013Proteomic profiling of cardiomyopathic tissue from the aged mdx model ofDuchenne muscular dystrophy reveals a drastic decrease in laminin, nidogen and annexinProteomics131523122323

91 

Holland A, Dowling P, Meleady P2015Label-free massspectrometric analysis of the mdx-4cv diaphragm identifies thematricellular protein periostin as a potential factor involved indystrophinopathy-related fibrosisProteomics151323182331

92 

Guevel L, Lavoie JR, Perez-Iratxeta C2011Quantitative proteomic analysis of dystrophic dog muscleJProteome Res10524652478

93 

Lewis C, Ohlendieck K2010Proteomic profiling of naturally protected extraocular muscles from the dystrophin-deficient mdx mouseBiochem Biophys Res Commun396410241029

94 

Nghiem PP, Hoffman EP, Mittal P2013Sparing of the dystrophin-deficient cranial sartorius muscle isassociated with classical and novel hypertrophy pathways in GRMD dogsAm J Pathol183514111424

95 

Alagaratnam S, Mertens BJ, Dalebout JC2008Serum protein profiling in mice: Identification of Factor XIIIa asa potential biomarker for muscular dystrophyProteomics8815521563

96 

Colussi C, Banfi C, Brioschi M2010Proteomic profile of differentially expressed plasma proteins fromdystrophic mice and following suberoylanilide hydroxamic acid treatmentProteomics Clin Appl417183

97 

Nadarajah VD, Mertens BJ, Dalebout H2012SerumPeptide Profiles of Duchenne Muscular Dystrophy (DMD) PatientsEvaluated by Data Handling Strategies for High Resolution ContentProteomics & Bioinformatics5496103

98 

Hathout Y, Marathi RL, Rayavarapu S2014Discovery of SerumProtein Biomarkers in the mdx mouse model and cross-speciescomparison to Duchenne Muscular Dystrophy patientsHum Mol Genet232464586469

99 

Vidal B, Serrano AL, Tjwa M2008Fibrinogen drives dystrophic muscle fibrosis via a TGFbeta/alternativemacrophage activation pathwayGenes Dev221317471752

100 

Nadarajah VD, van Putten M, Chaouch A2011Serum matrixmetalloproteinase-9 (MMP-9) as a biomarker for monitoring diseaseprogression in Duchenne muscular dystrophy (DMD)NeuromusculDisord218569578

101 

Cynthia MF, Hiller M, Spitali P2014Fibronectin is a serum biomarker for Duchenne muscular dystrophyProteomics Clin Appl83-4269278

102 

Rouillon J, Zocevic A, Leger T2014Proteomics profiling ofurine reveals specific titin fragments as biomarkers of Duchennemuscular dystrophyNeuromuscul Disord247563573

103 

Hathout Y, Brody E, Clemens PR2015Large-scale serum protein biomarker discovery in Duchenne musculardystrophyProc Natl Acad Sci U S A1122371537158

104 

Shapira YA, Patz D, Menczel J1984Low serum 24,25 dihydroxyvitamin D in Duchenne muscular dystrophyNeurology34911921196

105 

Pleasure D, Wyszynski B, Sumner A1979Skeletal muscle calcium metabolism and contractile force in vitaminD-deficient chicksJ Clin Invest64511571167

106 

Bulfield G, Siller WG, Wight PA1984X chromosome-linked muscular dystrophy (mdx) in the mouseProc Natl AcadSci U S A81411891192

107 

McIntosh LM, Garrett KL, Megeney L1998Regeneration and myogenic cell proliferation correlate with taurinelevels in dystrophin- and MyoD-deficient musclesAnat Rec2522311324

108 

Griffin JL, Williams HJ, Sang E2001Metabolic profiling of genetic disorders: A multitissue (1)H nuclearmagnetic resonance spectroscopic and pattern recognition study into dystrophic tissueAnal Biochem29311621

109 

Jones GL, Sang E, Goddard C2005A functional analysis of mouse models of cardiac disease through metabolicprofilingJ Biol Chem280975307539

110 

Griffin JL, Sang E, Evens T2002Metabolic profiles ofdystrophin and utrophin expression in mouse models of Duchennemuscular dystrophyFEBS Lett5301-3109116

111 

Franciotta D, Zanardi MC, Albertotti L2003Measurement of skeletal muscle mass in Duchenne musculardystrophy: Use of 24-h creatinine excretionActa Diabetol40 Suppl 1S290S292

112 

Nakagawa T, Takeuchi A, Kakiuchi R2013A prostaglandin D2 metabolite is elevated in the urine of Duchennemuscular dystrophy patients and increases further from 8 years oldClin Chim Acta4231014

113 

Srivastava NK, Pradhan S, Mittal B2010High resolution NMR based analysis of serum lipids in Duchennemuscular dystrophy patients and its possible diagnostic significanceNMR Biomed2311322

114 

Horster I, Weigt-Usinger K, Carmann C2015TheL-arginine/NO pathway and homoarginine are altered inDuchenne muscular dystrophy and improved by glucocorticoidsAminoAcidsJun 12. [Epub ahead of print]

115 

Gulston MK, Rubtsov DV, Atherton HJ2008A combined metabolomic and proteomic investigation of the effects ofa failure to express dystrophin in the mouse heartJ Proteome Res7520692077

116 

Griffin JL, Des RC2009Applications of metabolomics and proteomics to the mdx mouse model of Duchenne muscular dystrophy: Lessons from downstream of the transcriptomeGenome Med1332

117 

Ritchie MD, Holzinger ER, Li R2015Methods of integrating data to uncover genotype-phenotype interactionsNat Rev Genet1628597

118 

Marx V2015Cancer: Smoother journeys for molecular dataNat Methods124299302

119 

Forbes SC, Willcocks RJ, Triplett WT2014Magnetic resonance imaging and spectroscopy assessment of lowerextremity skeletal muscles in boys with Duchenne muscular dystrophy: A multicenter cross sectional studyPLoSOne99e106435

120 

Dittrich S, Tuerk M, Haaker G2015Cardiomyopathy in duchennemuscular dystrophy: Current value of clinical, electrophysiological and imaging findings in children andteenagersKlin Padiatr2274225231

121 

Johnson EK, Zhang L, Adams ME2012Proteomic analysis reveals new cardiac-specific dystrophin-associatedproteinsPLoS One78e43515

122 

van den Bergen JC, Wokke BH, Janson AA2014Dystrophin levels and clinical severity in Becker musculardystrophy patientsJ Neurol Neurosurg Psychiatry857747753

123 

Neri M, Torelli S, Brown S2007Dystrophin levels as low as 30% are sufficient to avoid muscular dystrophy inthe humanNeuromuscul Disord1711-12913918