A Study of a Cohort of X-Linked Myotubular Myopathy at the Clinical, Histologic, and Genetic Levels.

BACKGROUND: Myotubular myopathy is a rare X-linked congenital myopathy characterized by marked neonatal hypotonia and respiratory insufficiency, facial and ocular involvement, and muscle biopsy with prominent central nuclei in the majority of muscle fibers. It is caused by mutations in MTM1, which codes for the phosphoinositides phosphatase myotubularin. In this work, we established and detailed a new cohort of six patients at the clinical, histologic, and genetic levels. PATIENTS AND METHODS: Patients were recruited after screening 3065 muscle biopsy reports from two large biopsy banks in Sao Paulo, Brazil from the years 2008 to 2013, and from referrals to a neuromuscular outpatient clinic between 2011 and 2013. We reviewed biopsy slides, evaluated patients, and Sanger sequenced MTM1 in the families. RESULTS: All patients but one had classic phenotypes with a stable course after a severe onset. Two patients died suddenly from hypovolemic shock. Muscle biopsies had been performed in five patients, all of whom showed a classic pattern with a predominance of centrally located nuclei and increased oxidative activity in the center of the fibers. Two patients showed necklace fibers, and two families had novel truncating mutations in MTM1. CONCLUSIONS: X-linked myotubular myopathy is rare in the Brazilian population. Necklace fibers might be more prevalent in this condition than previously reported. Direct Sanger sequencing of MTM1 on clinical suspicion avoids the need of a muscle biopsy.

Integrative data mining highlights candidate genes for monogenic myopathies.

Inherited myopathies are a heterogeneous group of disabling disorders with still barely understood pathological mechanisms. Around 40% of afflicted patients remain without a molecular diagnosis after exclusion of known genes. The advent of high-throughput sequencing has opened avenues to the discovery of new implicated genes, but a working list of prioritized candidate genes is necessary to deal with the complexity of analyzing large-scale sequencing data. Here we used an integrative data mining strategy to analyze the genetic network linked to myopathies, derive specific signatures for inherited myopathy and related disorders, and identify and rank candidate genes for these groups. Training sets of genes were selected after literature review and used in Manteia, a public web-based data mining system, to extract disease group signatures in the form of enriched descriptor terms, which include functional annotation, human and mouse phenotypes, as well as biological pathways and protein interactions. These specific signatures were then used as an input to mine and rank candidate genes, followed by filtration against skeletal muscle expression and association with known diseases. Signatures and identified candidate genes highlight both potential common pathological mechanisms and allelic disease groups. Recent discoveries of gene associations to diseases, like B3GALNT2, GMPPB and B3GNT1 to congenital muscular dystrophies, were prioritized in the ranked lists, suggesting a posteriori validation of our approach and predictions. We show an example of how the ranked lists can be used to help analyze high-throughput sequencing data to identify candidate genes, and highlight the best candidate genes matching genomic regions linked to myopathies without known causative genes. This strategy can be automatized to generate fresh candidate gene lists, which help cope with database annotation updates as new knowledge is incorporated.