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Neurofilament levels are elevated in many neurodegenerative diseases and have shown promise as diagnostic and prognostic biomarkers in Amyotrophic Lateral Sclerosis (ALS), the most common form of Motor Neuron Disease (MND). This study assesses serum neurofilament light (NFL) and neurofilament heavy (NFH) chain concentrations in patients with ALS, other variants of motor neuron disease such as Progressive Muscular Atrophy (PMA) and Primary Lateral Sclerosis (PLS), and a range of other neurological diseases. It aims to evaluate the use of NFL and NFH to differentiate these conditions and for the prognosis of MND disease progression. NFL and NFH levels were quantified using electrochemiluminescence immunoassays (ECLIA). Both were elevated in 47 patients with MND compared to 34 patients with other neurological diseases and 33 healthy controls. NFL was able to differentiate patients with MND from the other groups with a Receiver Operating Characteristic (ROC) curve area under the curve (AUC) of 0.90 (p < 0.001). NFL correlated with the rate of disease progression in MND (rho 0.758, p < 0.001) and with the ALS Functional Rating Scale (rho −0.335, p = 0.021). NFL levels were higher in patients with ALS compared to both PMA (p = 0.032) and PLS (p = 0.012) and were able to distinguish ALS from both PMA and PLS with a ROC curve AUC of 0.767 (p = 0.005). These findings support the use of serum NFL to help diagnose and differentiate types of MND, in addition to providing prognostic information to patients and their families.

Amyotrophic lateral sclerosis (ALS) is a fatal late-onset motor neuron disease characterized by the loss of the upper and lower motor neurons. Our understanding of the molecular basis of ALS pathology remains elusive, complicating the development of efficient treatment. Gene-set analyses of genome-wide data have offered insight into the biological processes and pathways of complex diseases and can suggest new hypotheses regarding causal mechanisms. Our aim in this study was to identify and explore biological pathways and other gene sets having genomic association to ALS. Two cohorts of genomic data from the dbGaP repository were combined: (a) the largest available ALS individual-level genotype dataset (N = 12,319), and (b) a similarly sized control cohort (N = 13,210). Following comprehensive quality control pipelines, imputation and meta-analysis, we assembled a large European descent ALS-control cohort of 9244 ALS cases and 12,795 healthy controls represented by genetic variants of 19,242 genes. Multi-marker analysis of genomic annotation (MAGMA) gene-set analysis was applied to an extensive collection of 31,454 gene sets from the molecular signatures database (MSigDB). Statistically significant associations were observed for gene sets related to immune response, apoptosis, lipid metabolism, neuron differentiation, muscle cell function, synaptic plasticity and development. We also report novel interactions between gene sets, suggestive of mechanistic overlaps. A manual meta-categorization and enrichment mapping approach is used to explore the overlap of gene membership between significant gene sets, revealing a number of shared mechanisms.

Amyotrophic Lateral Sclerosis (ALS), Spinal Bulbar Muscular Atrophy (SBMA), and Spinal Muscular Atrophy (SMA) are motor neuron diseases (MNDs) characterised by progressive motor neuron degeneration, weakness and muscular atrophy. Lipid dysregulation is well recognised in each of these conditions and occurs prior to neurodegeneration. Several lipid markers have been shown to predict prognosis in ALS. Sphingolipids are complex lipids enriched in the central nervous system and are integral to key cellular functions including membrane stability and signalling pathways, as well as being mediators of neuroinflammation and neurodegeneration. This review highlights the metabolism of sphingomyelin (SM), the most abundant sphingolipid, and of its metabolite ceramide, and its role in the pathophysiology of neurodegeneration, focusing on MNDs. We also review published lipidomic studies in MNDs. In the 13 studies of patients with ALS, 12 demonstrated upregulation of multiple SM species and 6 demonstrated upregulation of ceramides. SM species also correlated with markers of clinical progression in five of six studies. These data highlight the potential use of SM and ceramide as biomarkers in ALS. Finally, we review potential therapeutic strategies for targeting sphingolipid metabolism in neurodegeneration.

Quality control of genomic data is an essential but complicated multi-step procedure, often requiring separate installation and expert familiarity with a combination of different bioinformatics tools. Software incompatibilities, and inconsistencies across computing environments, are recurrent challenges, leading to poor reproducibility. Existing semi-automated or automated solutions lack comprehensive quality checks, flexible workflow architecture, and user control. To address these challenges, we have developed snpQT: a scalable, stand-alone software pipeline using nextflow and BioContainers, for comprehensive, reproducible and interactive quality control of human genomic data. snpQT offers some 36 discrete quality filters or correction steps in a complete standardised pipeline, producing graphical reports to demonstrate the state of data before and after each quality control procedure. This includes human genome build conversion, population stratification against data from the 1,000 Genomes Project, automated population outlier removal, and built-in imputation with its own pre- and post- quality controls. Common input formats are used, and a synthetic dataset and comprehensive online tutorial are provided for testing, educational purposes, and demonstration. The snpQT pipeline is designed to run with minimal user input and coding experience; quality control steps are implemented with numerous user-modifiable thresholds, and workflows can be flexibly combined in custom combinations. snpQT is open source and freely available at https://github.com/nebfield/snpQT. A comprehensive online tutorial and installation guide is provided through to GWAS (https://snpqt.readthedocs.io/en/latest/), introducing snpQT using a synthetic demonstration dataset and a real-world Amyotrophic Lateral Sclerosis SNP-array dataset.

The field of neuromuscular research has seen considerable recent advances in the molecular and cellular understanding of muscle biology, and the treatment of neuromuscular disease [...]

Amyotrophic Lateral Sclerosis (ALS) is the most common late-onset motor neuron disorder, but our current knowledge of the molecular mechanisms and pathways underlying this disease remain elusive. This review (1) systematically identifies machine learning studies aimed at the understanding of the genetic architecture of ALS, (2) outlines the main challenges faced and compares the different approaches that have been used to confront them, and (3) compares the experimental designs and results produced by those approaches and describes their reproducibility in terms of biological results and the performances of the machine learning models. The majority of the collected studies incorporated prior knowledge of ALS into their feature selection approaches, and trained their machine learning models using genomic data combined with other types of mined knowledge including functional associations, protein-protein interactions, disease/tissue-specific information, epigenetic data, and known ALS phenotype-genotype associations. The importance of incorporating gene-gene interactions and cis-regulatory elements into the experimental design of future ALS machine learning studies is highlighted. Lastly, it is suggested that future advances in the genomic and machine learning fields will bring about a better understanding of ALS genetic architecture, and enable improved personalized approaches to this and other devastating and complex diseases.

Amyotrophic lateral sclerosis (ALS) is a terminal late-onset condition characterized by the loss of upper and lower motor neurons. Mutations in more than 30 genes are associated to the disease, but these explain only ~20% of cases. The molecular functions of these genes implicate a wide range of cellular processes in ALS pathology, a cohesive understanding of which may provide clues to common molecular mechanisms across both familial (inherited) and sporadic cases and could be key to the development of effective therapeutic approaches. Here, the different pathways that have been investigated in ALS are summarized, discussing in detail: mitochondrial dysfunction, oxidative stress, axonal transport dysregulation, glutamate excitotoxicity, endosomal and vesicular transport impairment, impaired protein homeostasis, and aberrant RNA metabolism. This review considers the mechanistic roles of ALS-associated genes in pathology, viewed through the prism of shared molecular pathways.

Background The approach of building large collections of gene sets and then systematically testing hypotheses across these collections is a powerful tool in functional genomics, both in the pathway analysis of omics data and to uncover the polygenic effects associated with complex diseases in genome-wide association study. The Molecular Signatures Database includes collections of oncogenic and immunologic signatures enabling researchers to compare transcriptional datasets across hundreds of previous studies and leading to important insights in these fields, but such a resource does not currently exist for neuromuscular research. In previous work, we have shown the utility of gene set approaches to understand muscle cell physiology and pathology. Methods Following a systematic survey of public muscle data, we passed gene expression profiles from 4305 samples through a robust pre-processing and standardized data analysis pipeline. Two hundred eighty-two samples were discarded based on a battery of rigorous global quality controls. From among the remaining studies, 578 comparisons of interest were identified by a combination of text mining and manual curation of the study meta-data. For each comparison, significantly dysregulated genes (FDR adjusted p

Multiple genes and mechanisms of pathophysiology have been implicated in amyotrophic lateral sclerosis (ALS), suggesting it is a complex systemic disease. With this in mind, applying personalized medicine (PM) approaches to tailor treatment pipelines for ALS patients may be necessary. The modelling and analysis of molecular interaction networks could represent valuable resources in defining ALS-associated pathways and discovering novel therapeutic targets. Here we review existing omics datasets and analytical approaches, in order to consider how molecular interaction networks could improve our understanding of the molecular pathophysiology of this fatal neuromuscular disorder.

Background hTERT/cdk4 immortalized myogenic human cell lines represent an important tool for skeletal muscle research, being used as therapeutically pertinent models of various neuromuscular disorders and in numerous fundamental studies of muscle cell function. However, the cell cycle is linked to other cellular processes such as integrin regulation, the PI3K/Akt pathway, and microtubule stability, raising the question as to whether genetic modification related to the cell cycle results in secondary effects that could undermine the validity of these cell models. Results Here we subjected five healthy and disease muscle cell isolates to transcriptomic analysis, comparing immortalized lines with their parent primary populations in both differentiated and undifferentiated states, and testing their myogenic character by comparison with non-myogenic (CD56-negative) cells. Principal component analysis of global gene expression showed tight clustering of immortalized myoblasts to their parent primary populations, with clean separation from the non-myogenic reference. Comparison was made to publicly available transcriptomic data from studies of muscle human pathology, cell, and animal models, including to derive a consensus set of genes previously shown to have altered regulation during myoblast differentiation. Hierarchical clustering of samples based on gene expression of this consensus set showed that immortalized lines retained the myogenic expression patterns of their parent primary populations. Of 2784 canonical pathways and gene ontology terms tested by gene set enrichment analysis, none were significantly enriched in immortalized compared to primary cell populations. We observed, at the whole transcriptome level, a strong signature of cell cycle shutdown associated with senescence in one primary myoblast population, whereas its immortalized clone was protected. Conclusions Immortalization had no observed effect on the myogenic cascade or on any other cellular processes, and it was protective against the systems level effects of senescence that are observed at higher division counts of primary cells. Electronic supplementary material The online version of this article (doi:10.1186/s13395-016-0115-5) contains supplementary material, which is available to authorized users.

International audience; The molecular mechanisms by which aging affects stem cell number and function are poorly understood. Murine data have implicated cellular senescence in the loss of muscle stem cells with aging. Here, using human cells and by carrying out experiments within a strictly pre-senescent division count, we demonstrate an impaired capacity for stem cell self-renewal in elderly muscle. We link aging to an increased methylation of the SPRY1 gene, a known regulator of muscle stem cell quiescence. Replenishment of the reserve cell pool was modulated experimentally by demethylation or siRNA knockdown of SPRY1. We propose that suppression of SPRY1 by age-associated methylation in humans inhibits the replenishment of the muscle stem cell pool, contributing to a decreased regenerative response in old age. We further show that aging does not affect muscle stem cell senescence in humans.

International audience; The use of antisense ‘splice-switching’ oligonucleotides to induce exon skipping represents a potential therapeutic approach to various human genetic diseases. It has achieved greatest maturity in exon skipping of the dystrophin transcript in Duchenne muscular dystrophy (DMD), for which several clinical trials are completed or ongoing, and a large body of data exists describing tested oligonucleotides and their efficacy. The rational design of an exon skipping oligonucleotide involves the choice of an antisense sequence, usually between 15 and 32 nucleotides, targeting the exon that is to be skipped. Although parameters describing the target site can be computationally estimated and several have been identified to correlate with efficacy, methods to predict efficacy are limited. Here, an in silico pre-screening approach is proposed, based on predictive statistical modelling. Previous DMD data were compiled together and, for each oligonucleotide, some 60 descriptors were considered. Statistical modelling approaches were applied to derive algorithms that predict exon skipping for a given target site. We confirmed (1) the binding energetics of the oligonucleotide to the RNA, and (2) the distance in bases of the target site from the splice acceptor site, as the two most predictive parameters, and we included these and several other parameters (while discounting many) into an in silico screening process, based on their capacity to predict high or low efficacy in either phosphorodiamidate morpholino oligomers (89% correctly predicted) and/or 2’O Methyl RNA oligonucleotides (76% correctly predicted). Predictions correlated strongly with in vitro testing for sixteen de novo PMO sequences targeting various positions on DMD exons 44 (R2 0.89) and 53 (R2 0.89), one of which represents a potential novel candidate for clinical trials. We provide these algorithms together with a computational tool that facilitates screening to predict exon skipping efficacy at each position of a target exon.

International audience; Given a query list of genes or proteins, CellWhere produces an interactive graphical display that mimics the structure of a cell, showing the local interaction network organized into subcellular locations. This user-friendly tool helps in the formulation of mechanistic hypotheses by enabling the experimental biologist to explore simultaneously two elements of functional context: (i) protein subcellular localization and (ii) protein–protein interactions or gene functional associations. Subcellular localization terms are obtained from public sources (the Gene Ontology and UniProt—together containing several thousand such terms) then mapped onto a smaller number of CellWhere localizations. These localizations include all major cell compartments, but the user may modify the mapping as desired. Protein–protein interaction listings, and their associated evidence strength scores, are obtained from the Mentha interactome server, or power-users may upload a pre-made network produced using some other interactomics tool. The Cytoscape.js JavaScript library is used in producing the graphical display. Importantly, for a protein that has been observed at multiple subcellular locations, users may prioritize the visual display of locations that are of special relevance to their research domain. CellWhere is at http://cellwhere-myology.rhcloud.com.

Transplantation of myogenic stem cells possesses great potential for long-term repair of dystrophic muscle. However, a single donor muscle biopsy is unlikely to provide enough cells to effectively transplant the muscle mass of a patient affected by muscular dystrophy. Expansion of cells ex vivo using traditional culture techniques significantly reduces engraftment potential. We hypothesized that activation of Notch signaling during ex vivo expansion would maintain donor cell engraftment potential. In this study, we expanded freshly isolated canine muscle-derived cells on tissue culture plates coated with Delta-1ext-IgG to activate Notch signaling or with human IgG as a control. A model of canine-to-murine xenotransplantation was used to quantitatively compare canine muscle cell engraftment and determine whether engrafted donor cells could function as satellite cells in vivo. We show that Delta-1ext-IgG inhibited differentiation of canine muscle-derived cells and increased the level of genes normally expressed in myogenic precursors. Moreover, cells expanded on Delta-1ext-IgG resulted in a significant increase in the number of donor-derived fibers, as compared to cells expanded on human IgG, reaching engraftment levels similar to freshly isolated cells. Importantly, cells expanded on Delta-1ext-IgG engrafted to the recipient satellite cell niche and contributed to further regeneration. A similar strategy of expanding human muscle-derived cells on Notch ligand might facilitate engraftment and muscle regeneration for patients affected with muscular dystrophy.

Research into muscle atrophy and hypertrophy is hampered by limitations of the available experimental models. Interpretation of in vivo experiments is confounded by the complexity of the environment while in vitro models are subject to the marked disparities between cultured myotubes and the mature myofibres of living tissues. Here we develop a method (PhAct) based on ex vivo maintenance of the isolated myofibre as a model of disuse atrophy, using standard microscopy equipment and widely available analysis software, to measure f-actin content per myofibre and per nucleus over two weeks of ex vivo maintenance. We characterize the 35% per week atrophy of the isolated myofibre in terms of early changes in gene expression and investigate the effects on loss of muscle mass of modulatory agents, including Myostatin and Follistatin. By tracing the incorporation of a nucleotide analogue we show that the observed atrophy is not associated with loss or replacement of myonuclei. Such a completely controlled investigation can be conducted with the myofibres of a single muscle. With this novel method we can distinguish those features and mechanisms of atrophy and hypertrophy that are intrinsic to the muscle fibre from those that include activities of other tissues and systemic agents.

The aim of this project was to build and apply tools for the analysis of muscle omics data, with a focus on Dysferlin deficiency. This protein is expressed mainly in skeletal and cardiac muscles, and its loss due to mutation (autosomal-recessive) of the DYSF gene, results in a progressive muscular dystrophy (Limb Girdle Muscular Dystrophy type 2B (LGMD2B), Miyoshi myopathy and distal myopathy with tibialis anterior onset (DMAT)). We have developed various tools and pipelines that can be applied towards a bioinformatics functional analysis of omics data in muscular dystrophies and neuromuscular disorders. These include: tests for enrichment of gene sets derived from previously published muscle microarray data and networking analysis of functional associations between altered transcripts/proteins. To accomplish this, we analyzed hundreds of published omics data from public repositories. The tools we developed are called CellWhere and MyoMiner. CellWhere is a user-friendly tool that combines protein-protein interactions and protein subcellular localizations on an interactive graphical display (https://cellwhere-myo.rhcloud.com). MyoMiner is a muscle cell- and tissue-specific database that provides co-expression analyses in both normal and pathological tissues. Many gene co-expression databases already exist and are used broadly by researchers, but MyoMiner is the first muscle-specific tool of its kind (https://myominer-myo.rhcloud.com). These tools will be used in the analysis and interpretation of transcriptomics data from dysferlinopathic muscle and other neuromuscular conditions and will be important to understand the molecular mechanisms underlying these pathologies.; Le but de mon projet est de créer et d’appliquer des outils pour l’analyse de la biologie des systèmes musculaires en utilisant différentes données OMICS. Ce projet s’intéresse plus particulièrement à la dysferlinopathie due la déficience d’une protéine appelée dysferline qui est exprimée principalement dans les muscles squelettiques et cardiaque. La perte du dysferline due à la mutation (autosomique-récessive) du gène DYSF entraîne une dystrophie musculaire progressive (LGMD2B, MM, DMAT). Nous avons déjà développé des outils bio-informatiques qui peuvent être utilisés pour l’analyse fonctionnelle de données OMICS, relative à la dyspherlinopathie. Ces derniers incluent le test dit «gene set enrichment analysis», test comparant les profils OMICS d’intérêts aux données OMICS musculaires préalablement publiées ; et l’analyse des réseaux impliquant les diffèrent(e)s protéines et transcrits entre eux/elles. Ainsi, nous avons analysé des centaines de données omiques publiées provenant d’archives publiques. Les outils informatiques que nous avons développés sont CellWhere et MyoMiner. CellWhere est un outil facile à utiliser, permettant de visualiser sur un graphe interactif à la fois les interactions protéine-protéine et la localisation subcellulaire des protéines. Myominer est une base de données spécialisée dans le tissu et les cellules musculaires, et qui fournit une analyse de co-expression, aussi bien dans les tissus sains que pathologiques. Ces outils seront utilisés dans l'analyse et l'interprétation de données transcriptomiques pour les dyspherlinopathies mais également les autres pathologies neuromusculaires.

The aim of this project was to build and apply tools for the analysis of muscle omics data, with a focus on Dysferlin deficiency. This protein is expressed mainly in skeletal and cardiac muscles, and its loss due to mutation (autosomal-recessive) of the DYSF gene, results in a progressive muscular dystrophy (Limb Girdle Muscular Dystrophy type 2B (LGMD2B), Miyoshi myopathy and distal myopathy with tibialis anterior onset (DMAT)). We have developed various tools and pipelines that can be applied towards a bioinformatics functional analysis of omics data in muscular dystrophies and neuromuscular disorders. These include: tests for enrichment of gene sets derived from previously published muscle microarray data and networking analysis of functional associations between altered transcripts/proteins. To accomplish this, we analyzed hundreds of published omics data from public repositories. The tools we developed are called CellWhere and MyoMiner. CellWhere is a user-friendly tool that combines protein-protein interactions and protein subcellular localizations on an interactive graphical display (https://cellwhere-myo.rhcloud.com). MyoMiner is a muscle cell- and tissue-specific database that provides co-expression analyses in both normal and pathological tissues. Many gene co-expression databases already exist and are used broadly by researchers, but MyoMiner is the first muscle-specific tool of its kind (https://myominer-myo.rhcloud.com). These tools will be used in the analysis and interpretation of transcriptomics data from dysferlinopathic muscle and other neuromuscular conditions and will be important to understand the molecular mechanisms underlying these pathologies., Le but de mon projet est de créer et d’appliquer des outils pour l’analyse de la biologie des systèmes musculaires en utilisant différentes données OMICS. Ce projet s’intéresse plus particulièrement à la dysferlinopathie due la déficience d’une protéine appelée dysferline qui est exprimée principalement dans les muscles squelettiques et cardiaque. La perte du dysferline due à la mutation (autosomique-récessive) du gène DYSF entraîne une dystrophie musculaire progressive (LGMD2B, MM, DMAT). Nous avons déjà développé des outils bio-informatiques qui peuvent être utilisés pour l’analyse fonctionnelle de données OMICS, relative à la dyspherlinopathie. Ces derniers incluent le test dit «gene set enrichment analysis», test comparant les profils OMICS d’intérêts aux données OMICS musculaires préalablement publiées ; et l’analyse des réseaux impliquant les diffèrent(e)s protéines et transcrits entre eux/elles. Ainsi, nous avons analysé des centaines de données omiques publiées provenant d’archives publiques. Les outils informatiques que nous avons développés sont CellWhere et MyoMiner. CellWhere est un outil facile à utiliser, permettant de visualiser sur un graphe interactif à la fois les interactions protéine-protéine et la localisation subcellulaire des protéines. Myominer est une base de données spécialisée dans le tissu et les cellules musculaires, et qui fournit une analyse de co-expression, aussi bien dans les tissus sains que pathologiques. Ces outils seront utilisés dans l'analyse et l'interprétation de données transcriptomiques pour les dyspherlinopathies mais également les autres pathologies neuromusculaires.