Desminopathy is a subtype of myofibrillar myopathy caused by desmin mutations and characterized by protein aggregates accumulating in muscle fibers. The aim of this study was to assess the protein composition of these aggregates. Aggregates and intact myofiber sections were obtained from skeletal muscle biopsies of five desminopathy patients by laser microdissection and analyzed by a label-free spectral count-based proteomic approach. We identified 397 proteins with 22 showing significantly higher spectral indices in aggregates (ratio >1.8, p < 0.05). Fifteen of these proteins not previously reported as specific aggregate components provide new insights regarding pathomechanisms of desminopathy. Results of proteomic analysis were supported by immunolocalization studies and parallel reaction monitoring. Three mutant desmin variants were detected directly on the protein level as components of the aggregates, suggesting their direct involvement in aggregate-formation and demonstrating for the first time that proteomic analysis can be used for direct identification of a disease-causing mutation in myofibrillar myopathy. Comparison of the proteomic results in desminopathy with our previous analysis of aggregate composition in filaminopathy, another myofibrillar myopathy subtype, allows to determine subtype-specific proteomic profile that facilitates identification of the specific disorder.

BackgroundDiagnosis of neuromuscular diseases in primary care is often challenging. Rare diseases such as Pompe disease are easily overlooked by the general practitioner. We therefore aimed to develop a diagnostic support tool using patient-oriented questions and combined data mining algorithms recognizing answer patterns in individuals with selected neuromuscular diseases. A multicenter prospective study for the proof of concept was conducted thereafter.MethodsFirst, 16 interviews with patients were conducted focusing on their pre-diagnostic observations and experiences. From these interviews, we developed a questionnaire with 46 items. Then, patients with diagnosed neuromuscular diseases as well as patients without such a disease answered the questionnaire to establish a database for data mining. For proof of concept, initially only six diagnoses were chosen (myotonic dystrophy and myotonia (MdMy), Pompe disease (MP), amyotrophic lateral sclerosis (ALS), polyneuropathy (PNP), spinal muscular atrophy (SMA), other neuromuscular diseases, and no neuromuscular disease (NND). A prospective study was performed to validate the automated malleable system, which included six different classification methods combined in a fusion algorithm proposing a final diagnosis. Finally, new diagnoses were incorporated into the system.ResultsIn total, questionnaires from 210 individuals were used to train the system. 89.5 % correct diagnoses were achieved during cross-validation. The sensitivity of the system was 93–97 % for individuals with MP, with MdMy and without neuromuscular diseases, but only 69 % in SMA and 81 % in ALS patients. In the prospective trial, 57/64 (89 %) diagnoses were predicted correctly by the computerized system. All questions, or rather all answers, increased the diagnostic accuracy of the system, with the best results reached by the fusion of different classifier methods. Receiver operating curve (ROC) and p-value analyses confirmed the results.ConclusionA questionnaire-based diagnostic support tool using data mining methods exhibited good results in predicting selected neuromuscular diseases. Due to the variety of neuromuscular diseases, additional studies are required to measure beneficial effects in the clinical setting.Electronic supplementary materialThe online version of this article (doi:10.1186/s12911-016-0268-5) contains supplementary material, which is available to authorized users.

Escherichia coli Nissle 1917 (EcN) bears a defect in its LPS biosynthesis leading to truncated variable oligosaccharide-antigen chains and a semi-rough phenotype. It is effectively inactivated by complement factors due to resolved serum resistance and is, therefore, safe as a probiotic strain, i.e. for the treatment of inflammatory gastrointestinal diseases. It is unknown whether the modification of LPS in EcN contributes to its probiotic properties. Purified LPS from EcN and wild-type LPS from uropathogenic E. coli W536 together with raw lysates of both strains were analyzed for their gene expression activity with human PBMCs measured by microarrays. Comparing the two LPS molecules and the two lysate variants with each other, respectively, no differences of transcriptional patterns were observed. However, when comparing LPS with lysate patterns, pro-inflammatory cytokine IL-12p40 was up-regulated by both LPS molecules and anti-inflammatory IL-10 by both lysates. The higher the lysate concentration, the higher IL-10 release from PBMCs, clearly exceeding LPS induced IL-12p40 release. Furthermore, inflammatory chemokine CCL24 (eotaxin) was down-regulated by lysates and quantitative real-time PCR revealed that EcN compared to wild-type LPS was 8 times stronger in down-regulation of CCL24. We conclude that truncated LPS may down-regulate CCL24-mediated inflammation and that EcN lysate contains as yet unidentified factors which preferably induce anti-inflammatory activity. Both effects may contribute to the probiotic properties of EcN.