Severe ACTA1-related nemaline myopathy: intranuclear rods, cytoplasmic bodies, and enlarged perinuclear space as characteristic pathological features on muscle biopsies.

peer reviewedNemaline myopathy (NM) is a muscle disorder with broad clinical and genetic heterogeneity. The clinical presentation of affected individuals ranges from severe perinatal muscle weakness to milder childhood-onset forms, and the disease course and prognosis depends on the gene and mutation type. To date, 14 causative genes have been identified, and ACTA1 accounts for more than half of the severe NM cases. ACTA1 encodes α-actin, one of the principal components of the contractile units in skeletal muscle. We established a homogenous cohort of ten unreported families with severe NM, and we provide clinical, genetic, histological, and ultrastructural data. The patients manifested antenatal or neonatal muscle weakness requiring permanent respiratory assistance, and most deceased within the first months of life. DNA sequencing identified known or novel ACTA1 mutations in all. Morphological analyses of the muscle biopsy specimens showed characteristic features of NM histopathology including cytoplasmic and intranuclear rods, cytoplasmic bodies, and major myofibrillar disorganization. We also detected structural anomalies of the perinuclear space, emphasizing a physiological contribution of skeletal muscle α-actin to nuclear shape. In-depth investigations of the nuclei confirmed an abnormal localization of lamin A/C, Nesprin-1, and Nesprin-2, forming the main constituents of the nuclear lamina and the LINC complex and ensuring nuclear envelope integrity. To validate the relevance of our findings, we examined muscle samples from three previously reported ACTA1 cases, and we identified the same set of structural aberrations. Moreover, we measured an increased expression of cardiac α-actin in the muscle samples from the patients with longer lifespan, indicating a potential compensatory effect. Overall, this study expands the genetic and morphological spectrum of severe ACTA1-related nemaline myopathy, improves molecular diagnosis, highlights the enlargement of the perinuclear space as an ultrastructural hallmark, and indicates a potential genotype/phenotype correlation

Impact of stroke unit in a public hospital on length of hospitalization and rate of early mortality of ischemic stroke patients

We ascertained whether a public health stroke unit reduces the length of hospitalization, the rate of inpatient fatality, and the mortality rate 30 days after the stroke. Methods We compared a cohort of stroke patients managed on a general neurology/medical ward with a similar cohort of stroke patients managed in a str oke unit. The in-patient fatality rates and 30-day mortality rates were analyzed. Results 729 patients were managed in the general ward and 344 were treated at a comprehensive stroke unit. The in-patient fatality rates were 14.7% for the general ward group and 6.9% for the stroke unit group (p<0.001). The overall mortality rate 30 days after stroke was 20.9% for general ward patients and 14.2% for stroke unit patients (p=0.005). Conclusions We observed reduced in-patient fatalities and 30-day mortality rates in patients managed in the stroke unit. There was no impact on the length of hospitalization

Integrative Data Mining Highlights Candidate Genes for Monogenic Myopathies

<div><p>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 <i>B3GALNT2, GMPPB</i> and <i>B3GNT1</i> to congenital muscular dystrophies, were prioritized in the ranked lists, suggesting <i>a posteriori</i> 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.</p></div

Graph representation of relationships of known genes.

<p>All known genes for the different disease groups were concurrently analyzed for matching terms in different ontologies. Nodes represent genes, and edges between two given nodes are depicted when the number of terms shared by the two connected genes is greater than a certain threshold. Edge width is proportional to the number of terms shared between two genes, and node size and color in a scale from green (lowest) to red (highest) is proportional to the number of associations of a gene in the graph. Closely related genes appear clustered together, and hubs in the graph appear centrally located. A: graph for combined terms from Gene Ontology (GO), Human Phenotype Ontology (HPO) and Interactions Annotation (IA), with a threshold of 30 matching terms. The cluster with a yellow background includes genes implicated in metabolic myopathies, the one with a red background groups congenital muscular dystrophy genes, and the cluster with a gray background represents genes associated with congenital myasthenic syndromes. B: graph for HPO terms with a threshold of 20 matching terms. C: graph for GO terms, with a threshold of 10 matching terms. Background colors correspond to clusters represented in A. D: IA terms with a threshold of 5 matching terms. The gray background highlights a cluster with gene that code subunits of cholinergic receptors, implicated in congenital myasthenic syndromes, the green one groups components of collagen VI, and the cluster with a blue background links elements of the contractile apparatus.</p

Resulting 29 variants after filtration of exome data of a patient affected with nemaline myopathy.

<p>An initial 86,333 variants were reduced to 250 using criteria on the variant level, which resulted in the 29 variants after exclusion of genes already ascribed to diseases and based on specificity of skeletal muscle expression. Variants are then sorted according to the gene ranking calculated for the congenital myopathy group.</p><p>Resulting 29 variants after filtration of exome data of a patient affected with nemaline myopathy.</p

Integrated data mining workflow.

<p>A signature of a disease group, composed of weighted terms, is generated from statistical analyses of genes already implicated in diseases of the group. Terms come from the three main annotation groups, GO (Gene Ontology), PO (Phenotype Ontology, an aggregate of Human Phenotype Ontology and Mammalian Phenotype Ontology) and IA (Interactions Annotation), are mined using Manteia and receive weights proportional to the their enrichment in the set of genes implicated in the disease group, as compared to the set of all genes in the human genome. Weights are attributed to terms so that annotation groups contribute equally to the composition of the signature. The signature of the disease group is then used to mine the genome for additional genes. Every gene in the genome receives a score equal to the sum of weights of terms that describe the gene if they match terms that define the disease group signature, for a maximum possible score of 3000. Further filtering steps mark genes that have low relative skeletal muscle expression or are annotated with known diseases.</p

Top 8 ranked candidate genes for each disease group.

<p>Candidate genes are not associated with disease (as per annotation in OMIM) and are expressed in skeletal muscle with at least 10% of the maximum expression in any tissue, except for congenital myasthenic syndromes, where there was no expression filtering.</p><p>Top 8 ranked candidate genes for each disease group.</p

Breakdown of disease groups and known associated genes.

<p>Breakdown of disease groups and known associated genes.</p