Diagnosis 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. First, 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. In 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. A 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.

Duchenne muscular dystrophy (DMD) is a severe progressive muscle disease that mainly affects boys due to X-linked recessive inheritance. In most affected individuals, MLPA or sequencing-based techniques detect deletions, duplications, or point mutations in the dystrophin-encoding DMD gene. However, in a small subset of patients clinically diagnosed with DMD, the molecular cause is not identified with these routine methods. Evaluation of the 60 DMD patients in our center revealed three cases without a known genetic cause. DNA samples of these patients were analyzed using whole-exome sequencing (WES) and, if unconclusive, optical genome mapping (OGM). WES led to a diagnosis in two cases: one patient was found to carry a splice mutation in the DMD gene that had not been identified during previous Sanger sequencing. In the second patient, we detected two variants in the fukutin gene (FKTN) that were presumed to be disease-causing. In the third patient, WES was unremarkable, but OGM identified an inversion disrupting the DMD gene (~1.28 Mb) that was subsequently confirmed with long-read sequencing. These results highlight the importance of reanalyzing unsolved cases using WES and demonstrate that OGM is a useful method for identifying large structural variants in cases with unremarkable exome sequencing.

High pre-existing antibodies against viral vectors reduce their functionality and may lead to adverse complications. To circumvent this problem in future gene therapy approaches, we tested the seroprevalence of a large range of human adenovirus types in patients with neuromuscular disorders (NMDs) to find appropriate viral vector candidates for gene replacement therapy for NMDs. Binding and neutralizing antibodies against 39 human adenovirus types were tested in the sera of 133 patients with NMDs and 76 healthy controls aged 17–92 years. The influence of age, sex, and NMDs on antibody levels was analyzed. The seroprevalence of different adenoviruses in the cohort varied widely. The highest levels of binding antibodies were detected against HAdV-D27, -C1, -D24, -D70, -B14, -C6, -D13, -B34, and -E4, whereas the lowest reactivity was detected against HAdV-F41, -A31, -B11, -D75, -D8, -D65, -D26, -D80, and -D17. The highest neutralizing reactivity was observed against HAdV-B3, -C2, -E4, -C1, -G52, -C5, and -F41, whereas the lowest neutralizing reactivity was observed against HAdV-D74, -B34, -D73, -B37, -D48, -D13, -D75, -D8, -B35, and -B16. We detected no influence of sex and only minor differences between different age groups. Importantly, there were no significant differences between healthy controls and patients with NMDs. Our data show that patients with NMDs have very similar levels of binding and neutralizing antibodies against HAdV compared to healthy individuals, and we identified HAdV-A31, -B16, -B34, -B35, -D8, -D37, -D48, -D73, -D74, -D75, and -D80 as promising candidates for future vector development due to their low binding and neutralizing antibody prevalence.

Background: Quantitative muscle MRI (qMRI) is a promising tool for evaluating and monitoring neuromuscular disorders (NMD). However, the application of different imaging protocols and processing pipelines restricts comparison between patient cohorts and disorders. In this qMRI study, we aim to compare dystrophic (limb-girdle muscular dystrophy), inflammatory (inclusion body myositis), and metabolic myopathy (Pompe disease) as well as patients with post-COVID-19 conditions suffering from myalgia to healthy controls. Methods: Ten subjects of each group underwent a 3T lower extremity muscle MRI, including a multi-echo, gradient-echo, Dixon-based sequence, a multi-echo, spin-echo (MESE) T2 mapping sequence, and a spin-echo EPI diffusion-weighted sequence. Furthermore, the following clinical assessments were performed: Quick Motor Function Measure, patient questionnaires for daily life activities, and 6-min walking distance. Results: Different involvement patterns of conspicuous qMRI parameters for different NMDs were observed. qMRI metrics correlated significantly with clinical assessments. Conclusions: qMRI metrics are suitable for evaluating patients with NMD since they show differences in muscular involvement in different NMDs and correlate with clinical assessments. Still, standardisation of acquisition and processing is needed for broad clinical use.

Pathogenic variants in the ryanodine receptor 1 (RYR1) gene are causative for a wide spectrum of muscular phenotypes, ranging from malignant hyperthermia over mild, non-progressive to severe congenital myopathy. Both autosomal dominant and recessive inheritance can occur, with the more severe forms usually showing recessive inheritance. However, genotype–phenotype correlations are complicated due to the large size of the gene and heterogeneous phenotypes. We present a 6-year-old patient with severe congenital myopathy, carrying a heterozygous pathogenic RYR1 variant inherited from the healthy mother. Through whole genome sequencing we identified a second, deep intronic RYR1 variant that has recently been described in another patient with severe congenital myopathy and shown to affect splicing. Segregation analyses confirmed the variants to be compound heterozygous. We compared our patient’s phenotype to that of the patient from the literature as well as five additional patients with compound heterozygous RYR1 variants from our center. The main overlapping features comprised congenital onset, predominant muscular hypotonia, and normal creatine kinase (CK) levels, while overall clinical expression varied substantially. Interestingly, both patients carrying the new intronic splice variant showed a very severe disease course. More widespread use of genome sequencing will open the way for better genotype–phenotype correlations.