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BackgroundDysferlinopathies are autosomal recessive disorders caused by mutations in the dysferlin (DYSF) gene encoding the dysferlin protein. DYSF mutations lead to a wide range of muscular phenotypes, with the most prominent being Miyoshi myopathy (MM) and limb girdle muscular dystrophy type 2B (LGMD2B).MethodsWe assessed the one-year-natural course of dysferlinopathy, and the safety and efficacy of deflazacort treatment in a double-blind, placebo-controlled cross-over trial. After one year of natural course without intervention, 25 patients with genetically defined dysferlinopathy were randomized to receive deflazacort and placebo for six months each (1 mg/kg/day in month one, 1 mg/kg every 2nd day during months two to six) in one of two treatment sequences.ResultsDuring one year of natural course, muscle strength declined about 2% as measured by CIDD (Clinical Investigation of Duchenne Dystrophy) score, and 76 Newton as measured by hand-held dynamometry. Deflazacort did not improve muscle strength. In contrast, there is a trend of worsening muscle strength under deflazacort treatment, which recovers after discontinuation of the study drug. During deflazacort treatment, patients showed a broad spectrum of steroid side effects.ConclusionDeflazacort is not an effective therapy for dysferlinopathies, and off-label use is not warranted. This is an important finding, since steroid treatment should not be administered in patients with dysferlinopathy, who may be often misdiagnosed as polymyositis.Trial registrationThis clinical trial was registered at http://www.ClincalTrials.gov, identifier: NCT00527228, and was always freely accessible to the public.

Taken together, our results indicate that both common and rare SLC2A3 variation impacting regulation of neuronal glucose utilization and energy homeostasis may result in neurocognitive deficits known to contribute to ADHD risk.

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Background Estimation of incidence in rare diseases is often challenging due to unspecific and incomplete coding and recording systems. Patient- and health care provider-driven data collections are held with different organizations behind firewalls to protect the privacy of patients. They tend to be fragmented, incomplete and their aggregation leads to further inaccuracies, as the duplicated records cannot easily be identified. We here report about a novel approach to evaluate the incidences of Duchenne muscular dystrophy (DMD) and spinal muscular atrophy (SMA) in Germany. Methods We performed a retrospective epidemiological study collecting data from patients with dystrophinopathies (DMD and Becker muscular dystrophy) and SMA born between 1995 and 2018. We invited all neuromuscular centers, genetic institutes and the patient registries for DMD and SMA in Germany to participate in the data collection. A novel web-based application for data entry was developed converting patient identifying information into a hash code. Duplicate entries were reliably allocated to the distinct patient. Results We collected 5409 data entries in our web-based database representing 1955 distinct patients with dystrophinopathies and 1287 patients with SMA. 55.0% of distinct patients were found in one of the 3 data sources only, while 32.0% were found in 2, and 13.0% in all 3 data sources. The highest number of SMA patients was reported by genetic testing laboratories, while for DMD the highest number was reported by the clinical specialist centers. After the removal of duplicate records, the highest yearly incidence for DMD was calculated as 2.57:10,000 in 2001 and the highest incidence for SMA as 1.36:10,000 in 2014. Conclusion With our novel approach (compliant with data protection regulations), we were able to identify unique patient records and estimate the incidence of DMD and SMA in Germany combining and de-duplicating data from patient registries, genetic institutes, and clinical care centers. Although we combined three different data sources, an unknown number of patients might not have been reported by any of these sources. Therefore, our results reflect the minimal incidence of these diseases. Electronic supplementary material The online version of this article (10.1186/s13023-019-1125-2) contains supplementary material, which is available to authorized users.

Multiple sequence alignments (MSAs) are one of the most important sources of information in sequence analysis. Many methods have been proposed to detect, extract and visualize their most significant properties. To the same extent that site-specific methods like sequence logos successfully visualize site conservations and sequence-based methods like clustering approaches detect relationships between sequences, both types of methods fail at revealing informational elements of MSAs at the level of sequence–site interactions, i.e. finding clusters of sequences and sites responsible for their clustering, which together account for a high fraction of the overall information of the MSA. To fill this gap, we present here a method that combines the Fisher score-based embedding of sequences from a profile hidden Markov model (pHMM) with correspondence analysis. This method is capable of detecting and visualizing group-specific or conflicting signals in an MSA and allows for a detailed explorative investigation of alignments of any size tractable by pHMMs. Applications of our methods are exemplified on an alignment of the Neisseria surface antigen LP2086, where it is used to detect sites of recombinatory horizontal gene transfer and on the vitamin K epoxide reductase family to distinguish between evolutionary and functional signals.

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