Orthogonal proteomics methods warrant the development of Duchenne muscular dystrophy biomarkers.

BACKGROUND: Molecular components in blood, such as proteins, are used as biomarkers to detect or predict disease states, guide clinical interventions and aid in the development of therapies. While multiplexing proteomics methods promote discovery of such biomarkers, their translation to clinical use is difficult due to the lack of substantial evidence regarding their reliability as quantifiable indicators of disease state or outcome. To overcome this challenge, a novel orthogonal strategy was developed and used to assess the reliability of biomarkers and analytically corroborate already identified serum biomarkers for Duchenne muscular dystrophy (DMD). DMD is a monogenic incurable disease characterized by progressive muscle damage that currently lacks reliable and specific disease monitoring tools. METHODS: Two technological platforms are used to detect and quantify the biomarkers in 72 longitudinally collected serum samples from DMD patients at 3 to 5 timepoints. Quantification of the biomarkers is achieved by detection of the same biomarker fragment either through interaction with validated antibodies in immuno-assays or through quantification of peptides by Parallel Reaction Monitoring Mass Spectrometry assay (PRM-MS). RESULTS: Five, out of ten biomarkers previously identified by affinity-based proteomics methods, were confirmed to be associated with DMD using the mass spectrometry-based method. Two biomarkers, carbonic anhydrase III and lactate dehydrogenase B, were quantified with two independent methods, sandwich immunoassays and PRM-MS, with Pearson correlations of 0.92 and 0.946 respectively. The median concentrations of CA3 and LDHB in DMD patients was elevated in comparison to those in healthy individuals by 35- and 3-fold, respectively. Levels of CA3 vary between 10.26 and 0.36 ng/ml in DMD patients whereas those of LDHB vary between 15.1 and 0.8 ng/ml. CONCLUSIONS: These results demonstrate that orthogonal assays can be used to assess the analytical reliability of biomarker quantification assays, providing a means to facilitate the translation of biomarkers to clinical practice. This strategy also warrants the development of the most relevant biomarkers, markers that can be reliably quantified with different proteomics methods.

Diffusion-tensor magnetic resonance imaging captures increased skeletal muscle fibre diameters in Becker muscular dystrophy.

BACKGROUND: Becker muscular dystrophy (BMD) is an X-linked disorder characterized by slow, progressive muscle damage and muscle weakness. Hallmarks include fibre-size variation and replacement of skeletal muscle with fibrous and adipose tissues, after repeated cycles of regeneration. Muscle histology can detect these features, but the required biopsies are invasive, are difficult to repeat and capture only small muscle volumes. Diffusion-tensor magnetic resonance imaging (DT-MRI) is a potential non-invasive alternative that can calculate muscle fibre diameters when applied with the novel random permeable barrier model (RPBM). In this study, we assessed muscle fibre diameters using DT-MRI in BMD patients and healthy controls and compared these with histology. METHODS: We included 13 BMD patients and 9 age-matched controls, who underwent water-fat MRI and DT-MRI at multiple diffusion times, allowing RPBM parameter estimation in the lower leg muscles. Tibialis anterior muscle biopsies were taken from the contralateral leg in 6 BMD patients who underwent DT-MRI and from an additional 32 BMD patients and 15 healthy controls. Laminin and Sirius-red stainings were performed to evaluate muscle fibre morphology and fibrosis. Twelve ambulant patients from the MRI cohort underwent the North Star ambulatory assessment, and 6-min walk, rise-from-floor and 10-m run/walk functional tests. RESULTS: RPBM fibre diameter was significantly larger in BMD patients (P = 0.015): mean (SD) = 68.0 (25.3) µm versus 59.4 (19.2) µm in controls. Inter-muscle differences were also observed (P ≤ 0.002). Both inter- and intra-individual RPBM fibre diameter variability were similar between groups. Laminin staining agreed with the RPBM, showing larger median fibre diameters in patients than in controls: 72.5 (7.9) versus 63.2 (6.9) µm, P = 0.006. However, despite showing similar inter-individual variation, patients showed more intra-individual fibre diameter variability than controls-mean variance (SD) = 34.2 (7.9) versus 21.4 (6.9) µm, P < 0.001-and larger fibrosis areas: median (interquartile range) = 21.7 (5.6)% versus 14.9 (3.4)%, P < 0.001. Despite good overall agreement of RPBM and laminin fibre diameters, they were not associated in patients who underwent DT-MRI and muscle biopsy, perhaps due to lack of colocalization of DT-MRI with biopsy samples. CONCLUSIONS: DT-MRI RPBM metrics agree with histology and can quantify changes in muscle fibre size that are associated with regeneration without the need for biopsies. They therefore show promise as imaging biomarkers for muscular dystrophies.

Multiomic characterization of disease progression in mice lacking dystrophin.

Duchenne muscular dystrophy (DMD) is caused by genetic mutations leading to lack of dystrophin in skeletal muscle. A better understanding of how objective biomarkers for DMD vary across subjects and over time is needed to model disease progression and response to therapy more effectively, both in pre-clinical and clinical research. We present an in-depth characterization of disease progression in 3 murine models of DMD by multiomic analysis of longitudinal trajectories between 6 and 30 weeks of age. Integration of RNA-seq, mass spectrometry-based metabolomic and lipidomic data obtained in muscle and blood samples by Multi-Omics Factor Analysis (MOFA) led to the identification of 8 latent factors that explained 78.8% of the variance in the multiomic dataset. Latent factors could discriminate dystrophic and healthy mice, as well as different time-points. MOFA enabled to connect the gene expression signature in dystrophic muscles, characterized by pro-fibrotic and energy metabolism alterations, to inflammation and lipid signatures in blood. Our results show that omic observations in blood can be directly related to skeletal muscle pathology in dystrophic muscle.

Longitudinal Assessment of Creatine Kinase, Creatine/Creatinineratio, and Myostatin as Monitoring Biomarkers in Becker Muscular Dystrophy.

BACKGROUND AND OBJECTIVES: The slow and variable disease progression of Becker muscular dystrophy (BMD) urges the development of biomarkers to facilitate clinical trials. We explored changes in 3 muscle-enriched biomarkers in serum of patients with BMD over 4-year time and studied associations with disease severity, disease progression, and dystrophin levels in BMD. METHODS: We quantitatively measured creatine kinase (CK) using the International Federation of Clinical Chemistry reference method, creatine/creatinineratio (Cr/Crn) using liquid chromatography-tandem mass spectrometry, and myostatin with ELISA in serum and assessed functional performance using the North Star Ambulatory Assessment (NSAA), 10-meter run velocity (TMRv), 6-Minute Walking Test (6MWT), and forced vital capacity in a 4-year prospective natural history study. Dystrophin levels were quantified in the tibialis anterior muscle using capillary Western immunoassay. The correlation between biomarkers, age, functional performance, mean annual change, and prediction of concurrent functional performance was analyzed using linear mixed models. RESULTS: Thirty-four patients with 106 visits were included. Eight patients were nonambulant at baseline. Cr/Crn and myostatin were highly patient specific (intraclass correlation coefficient for both = 0.960). Cr/Crn was strongly negatively correlated, whereas myostatin was strongly positively correlated with the NSAA, TMRv, and 6MWT (Cr/Crn rho = -0.869 to -0.801 and myostatin rho = 0.792 to 0.842, all p < 0.001). CK showed a negative association with age (p = 0.0002) but was not associated with patients' performance. Cr/Crn and myostatin correlated moderately with the average annual change of the 6MWT (rho = -0.532 and 0.555, p = 0.02). Dystrophin levels did not correlate with the selected biomarkers nor with performance. Cr/Crn, myostatin, and age could explain up to 75% of the variance of concurrent functional performance of the NSAA, TMRv, and 6MWT. DISCUSSION: Both Cr/Crn and myostatin could potentially serve as monitoring biomarkers in BMD, as higher Cr/Crn and lower myostatin were associated with lower motor performance and predictive of concurrent functional performance when combined with age. Future studies are needed to more precisely determine the context of use of these biomarkers.

Histone deacetylase inhibitors improve antisense-mediated exon-skipping efficacy in mdx mice.

Antisense-mediated exon skipping is one of the most promising therapeutic strategies for Duchenne muscular dystrophy (DMD), and some antisense oligonucleotide (ASO) drugs have already been approved by the US FDA despite their low efficacy. The potential of this therapy is still limited by several challenges, including the reduced expression of the dystrophin transcript and the strong 5'-3' imbalance in mutated transcripts. We therefore hypothesize that increasing histone acetylation using histone deacetylase inhibitors (HDACi) could correct the transcript imbalance, offering more available pre-mRNA target and ultimately increasing dystrophin rescue. Here, we evaluated the impact of such a combined therapy on the Dmd transcript imbalance phenomenon and on dystrophin restoration levels in mdx mice. Analysis of the Dmd transcript levels at different exon-exon junctions revealed a tendency to correct the 5'-3' imbalance phenomenon following treatment with HDACi. Significantly higher levels of dystrophin restoration (up to 74% increase) were obtained with givinostat and valproic acid compared with mice treated with ASO alone. Additionally, we demonstrate an increase in H3K9 acetylation in human myocytes after treatment with valproic acid. These findings indicate that HDACi can improve the therapeutic potential of exon-skipping approaches, offering promising perspectives for the treatment of DMD.

The neurocognitive profile of adults with Becker muscular dystrophy in the Netherlands.

BACKGROUND: In Becker muscular dystrophy evidence for neurocognitive and behavioral comorbidity is evolving. More insight into the extend of these problems is of great importance for early detection and remediation in clinical practice. OBJECTIVE: In this study we aimed to describe the neurocognitive and behavioral features of a Dutch adult cohort of BMD patients, and to evaluate correlations to motor function outcomes. METHODS: 28 adult BMD patients were included. Intelligence, executive functioning, verbal memory and reaction times were assessed cross-sectionally. Additionally, patients completed questionnaires on behavioral and emotional symptoms, psychosocial and executive functions. Results were compared to normative data and correlated with disease severity as measured by the 10-meter run/walk test and Performance of the Upper Limb version 1.2. RESULTS: 15 patients (53.6%) had a high educational level despite frequent grade repeating (48.3%) during primary or secondary school. Neuropsychological testing revealed that intellectual abilities, verbal memory, processing speed and executive functioning were statistically significant below average, but still within normal range. Regarding outcomes of the behavioral questionnaires, no significant differences were reported compared to the norm population. No relevant correlations with disease severity were found. CONCLUSIONS: This cohort of adult BMD patients exhibits minor cognitive impairments and no significant behavioral problems. The lower outcomes on processing speed and verbal memory, combined with the relatively high prevalence of grade repeating during primary and secondary school, implies that these minor impairments played a role in childhood. However, the on average high educational levels suggests that they grow out of their cognitive impairments with ageing.

Value of Global Longitudinal Strain for Identification and Monitoring of Left Ventricular Dysfunction in Becker Muscular Dystrophy.

Cardiac involvement is the main cause of death in Becker muscular dystrophy (BMD). Identification of left ventricular (LV) function is crucial, but standard echocardiographic measurements such as LV ejection fraction (LVEF) might not be sensitive enough to detect early myocardial dysfunction. We explored the value of LV global longitudinal strain (GLS) as a more accurate echocardiographic parameter to detect and monitor LV dysfunction in BMD. Furthermore, we studied possible factors associated with LV dysfunction and progression. A total of 40 patients with BMD (age 39.0 ± 13.2 years) and 21 matched controls were included. Clinical variables, pulmonary tests, serum biomarkers, and echocardiograms were collected at baseline and after 2 years. LV systolic function was assessed by LVEF and LV GLS; a significant progression in LV dysfunction was defined as an absolute LV GLS deterioration ≥15%. Responsiveness to cardiac disease progression was determined using standardized response means. Patients showed impaired LVEF and LV GLS compared with controls (p <0.001). Of interest, 31 patients (77.5%) showed impaired LV GLS (defined as greater than -18%), whereas only 24 patients (60%) had reduced LVEF. LV GLS and LVEF correlated with troponin I (ρ = 0.553 and -0.523) and N-terminal pro-b-type natriuretic peptide (ρ = 0.506 and -0.585), but not with skeletal muscle or pulmonary function. At follow-up (2.0 ± 0.5 years, n = 29), LV GLS worsened significantly (-1.3 ± 0.8%, p = 0.002, standardized response mean = 0.70, annually = 0.60%), whereas LVEF remained stable. No risk factors for LV dysfunction progression were identified. In BMD, LV GLS is frequently impaired and shows deterioration over time compared with LVEF. LV GLS could be used as a more sensitive parameter to identify and monitor LV dysfunction.

Penalized regression calibration: A method for the prediction of survival outcomes using complex longitudinal and high-dimensional data.

Longitudinal and high-dimensional measurements have become increasingly common in biomedical research. However, methods to predict survival outcomes using covariates that are both longitudinal and high-dimensional are currently missing. In this article, we propose penalized regression calibration (PRC), a method that can be employed to predict survival in such situations. PRC comprises three modeling steps: First, the trajectories described by the longitudinal predictors are flexibly modeled through the specification of multivariate mixed effects models. Second, subject-specific summaries of the longitudinal trajectories are derived from the fitted mixed models. Third, the time to event outcome is predicted using the subject-specific summaries as covariates in a penalized Cox model. To ensure a proper internal validation of the fitted PRC models, we furthermore develop a cluster bootstrap optimism correction procedure that allows to correct for the optimistic bias of apparent measures of predictiveness. PRC and the CBOCP are implemented in the R package pencal, available from CRAN. After studying the behavior of PRC via simulations, we conclude by illustrating an application of PRC to data from an observational study that involved patients affected by Duchenne muscular dystrophy, where the goal is predict time to loss of ambulation using longitudinal blood biomarkers.

Plasma lipidomic analysis shows a disease progression signature in mdx mice.

Duchenne muscular dystrophy (DMD) is a rare genetic disorder affecting paediatric patients. The disease course is characterized by loss of muscle mass, which is rapidly substituted by fibrotic and adipose tissue. Clinical and preclinical models have clarified the processes leading to muscle damage and myofiber degeneration. Analysis of the fat component is however emerging as more evidence shows how muscle fat fraction is associated with patient performance and prognosis. In this article we aimed to study whether alterations exist in the composition of lipids in plasma samples obtained from mouse models. Analysis of plasma samples was performed in 4 mouse models of DMD and wild-type mice by LC-MS. Longitudinal samplings of individual mice covering an observational period of 7 months were obtained to cover the different phases of the disease. We report clear elevation of glycerolipids and glycerophospholipids families in dystrophic mice compared to healthy mice. Triacylglycerols were the strongest contributors to the signatures in mice. Annotation of individual lipids confirmed the elevation of lipids belonging to these families as strongest discriminants between healthy and dystrophic mice. A few sphingolipids (such as ganglioside GM2, sphingomyelin and ceramide), sterol lipids (such as cholesteryl oleate and cholesteryl arachidonate) and a fatty acyl (stearic acid) were also found to be affected in dystrophic mice. Analysis of serum and plasma samples show how several lipids are affected in dystrophic mice affected by muscular dystrophy. This study sets the basis to further investigations to understand how the lipid signature relates to the disease biology and muscle performance.

Peripheral blood transcriptome profiling enables monitoring disease progression in dystrophic mice and patients.

DMD is a rare disorder characterized by progressive muscle degeneration and premature death. Therapy development is delayed by difficulties to monitor efficacy non-invasively in clinical trials. In this study, we used RNA-sequencing to describe the pathophysiological changes in skeletal muscle of 3 dystrophic mouse models. We show how dystrophic changes in muscle are reflected in blood by analyzing paired muscle and blood samples. Analysis of repeated blood measurements followed the dystrophic signature at five equally spaced time points over a period of seven months. Treatment with two antisense drugs harboring different levels of dystrophin recovery identified genes associated with safety and efficacy. Evaluation of the blood gene expression in a cohort of DMD patients enabled the comparison between preclinical models and patients, and the identification of genes associated with physical performance, treatment with corticosteroids and body measures. The presented results provide evidence that blood RNA-sequencing can serve as a tool to evaluate disease progression in dystrophic mice and patients, as well as to monitor response to (dystrophin-restoring) therapies in preclinical drug development and in clinical trials.

Pathway testing for longitudinal metabolomics.

We propose a top-down approach for pathway analysis of longitudinal metabolite data. We apply a score test based on a shared latent process mixed model which can identify pathways with differentially progressing metabolites. The strength of our approach is that it can handle unbalanced designs, deals with potential missing values in the longitudinal markers, and gives valid results even with small sample sizes. Contrary to bottom-up approaches, correlations between metabolites are explicitly modeled leveraging power gains. For large pathway sizes, a computationally efficient solution is proposed based on pseudo-likelihood methodology. We demonstrate the advantages of the proposed method in identification of differentially expressed pathways through simulation studies. Finally, longitudinal metabolite data from a mice experiment is analyzed to demonstrate our methodology.

Negative Binomial mixed models estimated with the maximum likelihood method can be used for longitudinal RNAseq data.

Time-course RNAseq experiments, where tissues are repeatedly collected from the same subjects, e.g. humans or animals over time or under several different experimental conditions, are becoming more popular due to the reducing sequencing costs. Such designs offer the great potential to identify genes that change over time or progress differently in time across experimental groups. Modelling of the longitudinal gene expression in such time-course RNAseq data is complicated by the serial correlations, missing values due to subject dropout or sequencing errors, long follow up with potentially non-linear progression in time and low number of subjects. Negative Binomial mixed models can address all these issues. However, such models under the maximum likelihood (ML) approach are less popular for RNAseq data due to convergence issues (see, e.g. [1]). We argue in this paper that it is the use of an inaccurate numerical integration method in combination with the typically small sample sizes which causes such mixed models to fail for a great portion of tested genes. We show that when we use the accurate adaptive Gaussian quadrature approach to approximate the integrals over the random-effects terms, we can successfully estimate the model parameters with the maximum likelihood method. Moreover, we show that the boostrap method can be used to preserve the type I error rate in small sample settings. We evaluate empirically the small sample properties of the test statistics and compare with state-of-the-art approaches. The method is applied on a longitudinal mice experiment to study the dynamics in Duchenne Muscular Dystrophy. Contact:s.tsonaka@lumc.nl Roula Tsonaka is an assistant professor at the Medical Statistics, Department of Biomedical Data Sciences, Leiden University Medical Center. Her research focuses on statistical methods for longitudinal omics data. Pietro Spitali is an assistant professor at the Department of Human Genetics, Leiden University Medical Center. His research focuses on the identification of biomarkers for neuromuscular disorders.

The mRNA Binding Proteome of Proliferating and Differentiated Muscle Cells.

Muscle formation is a coordinated process driven by extensive gene expression changes where single cells fuse together to form multinucleated muscle fibers. Newly synthesized mRNAs are then regulated by RNA binding proteins (RBPs), affecting post-transcriptional transcript metabolism. Here, we determined how large-scale gene expression changes affect the catalog of RBPs by studying proliferating and differentiated muscle cells in healthy and dystrophic conditions. Transcriptomic analysis showed that the expression of more than 7000 genes was affected during myogenesis. We identified 769 RBPs, of which 294 were muscle-specific and 49 were uniquely shared with cardiomyocytes. A subset of 32 RBPs (half of which were muscle-specific) was found to be preferentially associated with target mRNAs in either myoblasts (MBs) or myotubes (MTs). A large proportion of catalytic proteins were bound to mRNAs even though they lack classical RNA binding domains. Finally, we showed how the identification of cell-specific RBPs enabled the identification of biomarkers that can separate healthy individuals from dystrophic patients. Our data show how interactome data can shed light on new basic RNA biology as well as provide cell-specific data that can be used for diagnostic purposes.

Tumor Necrosis Factor Receptor SF10A (TNFRSF10A) SNPs Correlate With Corticosteroid Response in Duchenne Muscular Dystrophy.

BACKGROUND: Duchenne muscular dystrophy (DMD) is a rare and severe X-linked muscular dystrophy in which the standard of care with variable outcome, also due to different drug response, is chronic off-label treatment with corticosteroids (CS). In order to search for SNP biomarkers for corticosteroid responsiveness, we genotyped variants across 205 DMD-related genes in patients with differential response to steroid treatment. METHODS AND FINDINGS: We enrolled a total of 228 DMD patients with identified dystrophin mutations, 78 of these patients have been under corticosteroid treatment for at least 5 years. DMD patients were defined as high responders (HR) if they had maintained the ability to walk after 15 years of age and low responders (LR) for those who had lost ambulation before the age of 10 despite corticosteroid therapy. Based on interactome mapping, we prioritized 205 genes and sequenced them in 21 DMD patients (discovery cohort or DiC = 21). We identified 43 SNPs that discriminate between HR and LR. Discriminant Analysis of Principal Components (DAPC) prioritized 2 response-associated SNPs in the TNFRSF10A gene. Validation of this genotype was done in two additional larger cohorts composed of 46 DMD patients on corticosteroid therapy (validation cohorts or VaC1), and 150 non ambulant DMD patients and never treated with corticosteroids (VaC2). SNP analysis in all validation cohorts (N = 207) showed that the CT haplotype is significantly associated with HR DMDs confirming the discovery results. CONCLUSION: We have shown that TNFRSF10A CT haplotype correlates with corticosteroid response in DMD patients and propose it as an exploratory CS response biomarker.

Premature termination codons in the DMD gene cause reduced local mRNA synthesis.

Duchenne muscular dystrophy (DMD) is caused by mutations in the DMD gene leading to the presence of premature termination codons (PTC). Previous transcriptional studies have shown reduced DMD transcript levels in DMD patient and animal model muscles when PTC are present. Nonsense-mediated decay (NMD) has been suggested to be responsible for the observed reduction, but there is no experimental evidence supporting this claim. In this study, we aimed to investigate the mechanism responsible for the drop in DMD expression levels in the presence of PTC. We observed that the inhibition of NMD does not normalize DMD gene expression in DMD. Additionally, in situ hybridization showed that DMD messenger RNA primarily localizes in the nuclear compartment, confirming that a cytoplasmic mechanism like NMD indeed cannot be responsible for the observed reduction. Sequencing of nascent RNA to explore DMD transcription dynamics revealed a lower rate of DMD transcription in patient-derived myotubes compared to healthy controls, suggesting a transcriptional mechanism involved in reduced DMD transcript levels. Chromatin immunoprecipitation in muscle showed increased levels of the repressive histone mark H3K9me3 in mdx mice compared to wild-type mice, indicating a chromatin conformation less prone to transcription in mdx mice. In line with this finding, treatment with the histone deacetylase inhibitor givinostat caused a significant increase in DMD transcript expression in mdx mice. Overall, our findings show that transcription dynamics across the DMD locus are affected by the presence of PTC, hinting at a possible epigenetic mechanism responsible for this process.

Rimeporide as a first- in-class NHE-1 inhibitor: Results of a phase Ib trial in young patients with Duchenne Muscular Dystrophy.

Rimeporide, a first-in-class sodium/proton exchanger Type 1 inhibitor (NHE-1 inhibitor) is repositioned by EspeRare for patients with Duchenne Muscular Dystrophy (DMD). Historically, NHE-1 inhibitors were developed for cardiac therapeutic interventions. There is considerable overlap in the pathophysiological mechanisms in Congestive Heart Failure (CHF) and in cardiomyopathy in DMD, therefore NHE-1 inhibition could be a promising pharmacological approach to the cardiac dysfunctions observed in DMD. Extensive preclinical data was collected in various animal models including dystrophin-deficient (mdx) mice to characterise Rimeporide's anti-fibrotic and anti-inflammatory properties and there is evidence that NHE-1 inhibitors could play a significant role in modifying DMD cardiac and also skeletal pathologies, as the NHE-1 isoform is ubiquitous. We report here the first study with Rimeporide in DMD patients. This 4-week treatment, open label phase Ib, multiple oral ascending dose study, enrolled 20 ambulant boys with DMD (6-11 years), with outcomes including safety, pharmacokinetic (PK) and pharmacodynamic (PD) biomarkers. Rimeporide was safe and well-tolerated at all doses. PK evaluations showed that Rimeporide was well absorbed orally reaching pharmacological concentrations from the lowest dose, with exposure increasing linearly with dose and with no evidence of accumulation upon repeated dosing. Exploratory PD biomarkers showed positive effect upon a 4-week treatment, supporting its therapeutic potential in patients with DMD, primarily as a cardioprotective treatment, and provide rationale for further efficacy studies.

Blood-derived biomarkers correlate with clinical progression in Duchenne muscular dystrophy.

BACKGROUND: Duchenne Muscular Dystrophy is a severe, incurable disorder caused by mutations in the dystrophin gene. The disease is characterized by decreased muscle function, impaired muscle regeneration and increased inflammation. In a clinical context, muscle deterioration, is evaluated using physical tests and analysis of muscle biopsies, which fail to accurately monitor the disease progression. OBJECTIVES: This study aims to confirm and asses the value of blood protein biomarkers as disease progression markers using one of the largest longitudinal collection of samples. METHODS: A total of 560 samples, both serum and plasma, collected at three clinical sites are analyzed using a suspension bead array platform to assess 118 proteins targeted by 250 antibodies in microliter amount of samples. RESULTS: Nine proteins are confirmed as disease progression biomarkers in both plasma and serum. Abundance of these biomarkers decreases as the disease progresses but follows different trajectories. While carbonic anhydrase 3, microtubule associated protein 4 and collagen type I alpha 1 chain decline rather constantly over time, myosin light chain 3, electron transfer flavoprotein A, troponin T, malate dehydrogenase 2, lactate dehydrogenase B and nestin plateaus in early teens. Electron transfer flavoprotein A, correlates with the outcome of 6-minutes-walking-test whereas malate dehydrogenase 2 together with myosin light chain 3, carbonic anhydrase 3 and nestin correlate with respiratory capacity. CONCLUSIONS: Nine biomarkers have been identified that correlate with disease milestones, functional tests and respiratory capacity. Together these biomarkers recapitulate different stages of the disorder that, if validated can improve disease progression monitoring.

Longitudinal metabolomic analysis of plasma enables modeling disease progression in Duchenne muscular dystrophy mouse models.

Duchenne muscular dystrophy is a severe pediatric neuromuscular disorder caused by the lack of dystrophin. Identification of biomarkers is needed to support and accelerate drug development. Alterations of metabolites levels in muscle and plasma have been reported in pre-clinical and clinical cross-sectional comparisons. We present here a 7-month longitudinal study comparing plasma metabolomic data in wild-type and mdx mice. A mass spectrometry approach was used to study metabolites in up to five time points per mouse at 6, 12, 18, 24 and 30 weeks of age, providing an unprecedented in depth view of disease trajectories. A total of 106 metabolites were studied. We report a signature of 31 metabolites able to discriminate between healthy and disease at various stages of the disease, covering the acute phase of muscle degeneration and regeneration up to the deteriorating phase. We show how metabolites related to energy production and chachexia (e.g. glutamine) are affected in mdx mice plasma over time. We further show how the signature is connected to molecular targets of nutraceuticals and pharmaceutical compounds currently in development as well as to the nitric oxide synthase pathway (e.g. arginine and citrulline). Finally, we evaluate the signature in a second longitudinal study in three independent mouse models carrying 0, 1 or 2 functional copies of the dystrophin paralog utrophin. In conclusion, we report an in-depth metabolomic signature covering previously identified associations and new associations, which enables drug developers to peripherally assess the effect of drugs on the metabolic status of dystrophic mice.

TCTEX1D1 is a genetic modifier of disease progression in Duchenne muscular dystrophy.

Duchenne muscular dystrophy (DMD) is caused by pathogenic variants in the DMD gene leading to the lack of dystrophin. Variability in the disease course suggests that other factors influence disease progression. With this study we aimed to identify genetic factors that may account for some of the variability in the clinical presentation. We compared whole-exome sequencing (WES) data in 27 DMD patients with extreme phenotypes to identify candidate variants that could affect disease progression. Validation of the candidate SNPs was performed in two independent cohorts including 301 (BIO-NMD cohort) and 109 (CINRG cohort of European ancestry) DMD patients, respectively. Variants in the Tctex1 domain containing 1 (TCTEX1D1) gene on chromosome 1 were associated with age of ambulation loss. The minor alleles of two independent variants, known to affect TCTEX1D1 coding sequence and induce skipping of its exon 4, were associated with earlier loss of ambulation. Our data show that disease progression of DMD is affected by a new locus on chromosome 1 and demonstrate the possibility to identify genetic modifiers in rare diseases by studying WES data in patients with extreme phenotypes followed by multiple layers of validation.

Simultaneous Enrichment Analysis of all Possible Gene-sets: Unifying Self-Contained and Competitive Methods.

Studying sets of genomic features is increasingly popular in genomics, proteomics and metabolomics since analyzing at set level not only creates a natural connection to biological knowledge but also offers more statistical power. Currently, there are two gene-set testing approaches, self-contained and competitive, both of which have their advantages and disadvantages, but neither offers the final solution. We introduce simultaneous enrichment analysis (SEA), a new approach for analysis of feature sets in genomics and other omics based on a new unified null hypothesis, which includes the self-contained and competitive null hypotheses as special cases. We employ closed testing using Simes tests to test this new hypothesis. For every feature set, the proportion of active features is estimated, and a confidence bound is provided. Also, for every unified null hypotheses, a $P$-value is calculated, which is adjusted for family-wise error rate. SEA does not need to assume that the features are independent. Moreover, users are allowed to choose the feature set(s) of interest after observing the data. We develop a novel pipeline and apply it on RNA-seq data of dystrophin-deficient mdx mice, showcasing the flexibility of the method. Finally, the power properties of the method are evaluated through simulation studies.