Transcriptome-scale similarities between mouse and human skeletal muscles with normal and myopathic phenotypes

BACKGROUND: Mouse and human skeletal muscle transcriptome profiles vary by muscle type, raising the question of which mouse muscle groups have the greatest molecular similarities to human skeletal muscle. METHODS: Orthologous (whole, sub-) transcriptome profiles were compared among four mouse-human transcriptome datasets: (M) six muscle groups obtained from three mouse strains (wildtype, mdx, mdx(5cv)); (H1) biopsied human quadriceps from controls and Duchenne muscular dystrophy patients; (H2) four different control human muscle types obtained at autopsy; and (H3) 12 different control human tissues (ten non-muscle). RESULTS: Of the six mouse muscles examined, mouse soleus bore the greatest molecular similarities to human skeletal muscles, independent of the latters' anatomic location/muscle type, disease state, age and sampling method (autopsy versus biopsy). Significant similarity to any one mouse muscle group was not observed for non-muscle human tissues (dataset H3), indicating this finding to be muscle specific. CONCLUSION: This observation may be partly explained by the higher type I fiber content of soleus relative to the other mouse muscles sampled

Meta-analysis of muscle transcriptome data using the MADMuscle database reveals biologically relevant gene patterns

<p>Abstract</p> <p>Background</p> <p>DNA microarray technology has had a great impact on muscle research and microarray gene expression data has been widely used to identify gene signatures characteristic of the studied conditions. With the rapid accumulation of muscle microarray data, it is of great interest to understand how to compare and combine data across multiple studies. Meta-analysis of transcriptome data is a valuable method to achieve it. It enables to highlight conserved gene signatures between multiple independent studies. However, using it is made difficult by the diversity of the available data: different microarray platforms, different gene nomenclature, different species studied, etc.</p> <p>Description</p> <p>We have developed a system tool dedicated to muscle transcriptome data. This system comprises a collection of microarray data as well as a query tool. This latter allows the user to extract similar clusters of co-expressed genes from the database, using an input gene list. Common and relevant gene signatures can thus be searched more easily. The dedicated database consists in a large compendium of public data (more than 500 data sets) related to muscle (skeletal and heart). These studies included seven different animal species from invertebrates (<it>Drosophila melanogaster, Caenorhabditis elegans</it>) and vertebrates (<it>Homo sapiens, Mus musculus, Rattus norvegicus, Canis familiaris, Gallus gallus</it>). After a renormalization step, clusters of co-expressed genes were identified in each dataset. The lists of co-expressed genes were annotated using a unified re-annotation procedure. These gene lists were compared to find significant overlaps between studies.</p> <p>Conclusions</p> <p>Applied to this large compendium of data sets, meta-analyses demonstrated that conserved patterns between species could be identified. Focusing on a specific pathology (Duchenne Muscular Dystrophy) we validated results across independent studies and revealed robust biomarkers and new pathways of interest. The meta-analyses performed with MADMuscle show the usefulness of this approach. Our method can be applied to all public transcriptome data.</p

Finding consistent disease subnetworks across microarray datasets

<p>Abstract</p> <p>Background</p> <p>While contemporary methods of microarray analysis are excellent tools for studying individual microarray datasets, they have a tendency to produce different results from different datasets of the same disease. We aim to solve this reproducibility problem by introducing a technique (SNet). SNet provides both quantitative and descriptive analysis of microarray datasets by identifying specific connected portions of pathways that are significant. We term such portions within pathways as “subnetworks”.</p> <p>Results</p> <p>We tested SNet on independent datasets of several diseases, including childhood ALL, DMD and lung cancer. For each of these diseases, we obtained two independent microarray datasets produced by distinct labs on distinct platforms. In each case, our technique consistently produced almost the same list of significant nontrivial subnetworks from two independent sets of microarray data. The gene-level agreement of these significant subnetworks was between 51.18% to 93.01%. In contrast, when the same pairs of microarray datasets were analysed using GSEA, t-test and SAM, this percentage fell between 2.38% to 28.90% for GSEA, 49.60% tp 73.01% for t-test, and 49.96% to 81.25% for SAM. Furthermore, the genes selected using these existing methods did not form subnetworks of substantial size. Thus it is more probable that the subnetworks selected by our technique can provide the researcher with more descriptive information on the portions of the pathway actually affected by the disease.</p> <p>Conclusions</p> <p>These results clearly demonstrate that our technique generates significant subnetworks and genes that are more consistent and reproducible across datasets compared to the other popular methods available (GSEA, t-test and SAM). The large size of subnetworks which we generate indicates that they are generally more biologically significant (less likely to be spurious). In addition, we have chosen two sample subnetworks and validated them with references from biological literature. This shows that our algorithm is capable of generating descriptive biologically conclusions.</p

Different Molecular Signatures in Magnetic Resonance Imaging-Staged Facioscapulohumeral Muscular Dystrophy Muscles

Facioscapulohumeral muscular dystrophy (FSHD) is one of the most common muscular dystrophies and is characterized by a non-conventional genetic mechanism activated by pathogenic D4Z4 repeat contractions. By muscle Magnetic Resonance Imaging (MRI) we observed that T2-short tau inversion recovery (T2-STIR) sequences identify two different conditions in which each muscle can be found before the irreversible dystrophic alteration, marked as T1-weighted sequence hyperintensity, takes place. We studied these conditions in order to obtain further information on the molecular mechanisms involved in the selective wasting of single muscles or muscle groups in this disease

Overexpression of microRNA-206 in the skeletal muscle from myotonic dystrophy type 1 patients

<p>Abstract</p> <p>Background</p> <p>MicroRNAs are highly conserved, noncoding RNAs involved in post-transcriptional gene silencing. They have been shown to participate in a wide range of biological processes, including myogenesis and muscle regeneration. The goal of this study is to test the hypothesis that myo-miRs (myo = muscle + miR = miRNA) expression is altered in muscle from patients affected by myotonic dystrophy type 1 (DM1), the most frequently inherited neuromuscular disease in adults. In order to gain better insights about the role of miRNAs in the DM1 pathogenesis, we have also analyzed the muscular expression of miR-103 and miR-107, which have been identified <it>in silico </it>as attractive candidates for binding to the <it>DMPK </it>mRNA.</p> <p>Methods</p> <p>To this aim, we have profiled the expression of miR-133 (miR-133a, miR-133b), miR-1, miR-181 (miR-181a, miR-181b, miR-181c) and miR-206, that are specifically induced during myogenesis in cardiac and skeletal muscle tissues. miR-103 and miR-107, highly expressed in brain, heart and muscle have also been included in this study. QRT-PCR experiments have been performed on RNA from vastus lateralis biopsies of DM1 patients (n = 7) and control subjects (n = 4). Results of miRNAs expression have been confirmed by Northern blot, whereas <it>in situ </it>hybridization technique have been performed to localize misexpressed miRNAs on muscle sections from DM1 and control individuals.</p> <p>Results</p> <p>Only miR-206 showed an over-expression in 5 of 7 DM1 patients (threshold = 2, fold change between 1.20 and 13.22, average = 5.37) compared to the control group. This result has been further confirmed by Northern blot analysis (3.37-fold overexpression, <it>R</it>²= 0.89). <it>In situ </it>hybridization localized miR-206 to nuclear site both in normal and DM1 tissues. Cellular distribution in DM1 tissues includes also the nuclear regions of centralized nuclei, with a strong signal corresponding to nuclear clumps.</p> <p>Conclusions</p> <p>This work provides, for the first time, evidences about miRNAs misexpression in DM1 muscle tissues, adding a new element in the pathogenesis of this complex genetic disease.</p

ccdc80-l1 Is Involved in Axon Pathfinding of Zebrafish Motoneurons

Axon pathfinding is a subfield of neural development by which neurons send out axons to reach the correct targets. In particular, motoneurons extend their axons toward skeletal muscles, leading to spontaneous motor activity. In this study, we identified the zebrafish Ccdc80 and Ccdc80-like1 (Ccdc80-l1) proteins in silico on the basis of their high aminoacidic sequence identity with the human CCDC80 (Coiled-Coil Domain Containing 80). We focused on ccdc80-l1 gene that is expressed in nervous and non-nervous tissues, in particular in territories correlated with axonal migration, such as adaxial cells and muscle pioneers. Loss of ccdc80-l1 in zebrafish embryos induced motility issues, although somitogenesis and myogenesis were not impaired. Our results strongly suggest that ccdc80-l1 is involved in axon guidance of primary and secondary motoneurons populations, but not in their proper formation. ccdc80-l1 has a differential role as regards the development of ventral and dorsal motoneurons, and this is consistent with the asymmetric distribution of the transcript. The axonal migration defects observed in ccdc80-l1 loss-of-function embryos are similar to the phenotype of several mutants with altered Hedgehog activity. Indeed, we reported that ccdc80-l1 expression is positively regulated by the Hedgehog pathway in adaxial cells and muscle pioneers. These findings strongly indicate ccdc80-l1 as a down-stream effector of the Hedgehog pathway

Unsupervised assessment of microarray data quality using a Gaussian mixture model

<p>Abstract</p> <p>Background</p> <p>Quality assessment of microarray data is an important and often challenging aspect of gene expression analysis. This task frequently involves the examination of a variety of summary statistics and diagnostic plots. The interpretation of these diagnostics is often subjective, and generally requires careful expert scrutiny.</p> <p>Results</p> <p>We show how an unsupervised classification technique based on the Expectation-Maximization (EM) algorithm and the naïve Bayes model can be used to automate microarray quality assessment. The method is flexible and can be easily adapted to accommodate alternate quality statistics and platforms. We evaluate our approach using Affymetrix 3' gene expression and exon arrays and compare the performance of this method to a similar supervised approach.</p> <p>Conclusion</p> <p>This research illustrates the efficacy of an unsupervised classification approach for the purpose of automated microarray data quality assessment. Since our approach requires only unannotated training data, it is easy to customize and to keep up-to-date as technology evolves. In contrast to other "black box" classification systems, this method also allows for intuitive explanations.</p