Background: Primary focal dystonia (PFD) is characterized by motor symptoms.

The cerebellar nuclei are a brain region with high iron content. Surprisingly little is known about iron content in the cerebellar nuclei and its possible contribution to pathology in cerebellar ataxias, with the only exception of Friedreich’s ataxia. In the present exploratory cross-sectional study, quantitative susceptibility mapping was used to investigate volume, iron concentration and total iron content of the dentate nuclei in common types of hereditary and non-hereditary degenerative ataxias. Seventy-nine patients with spinocerebellar ataxias of type 1 (SCA1), 2 (SCA2), 3 (SCA3) and 6 (SCA6), fifteen patients with Friedreich’s ataxia, eighteen patients with multiple system atrophy, cerebellar type (MSA-C), and one hundred eleven healthy controls were included. All underwent 3T-MRI and clinical assessments. For each specific ataxia subtype, voxel-based and volumes-of-interest-based group analyses were performed in comparison to a corresponding age- and sex-matched control group, both for volume, magnetic susceptiblity (indicating iron concentration) and susceptibility mass (indicating total iron content) of the dentate nuclei. SCA1 and MSA-C patients showed higher susceptibilities in large parts of the dentate nucleus but unaltered susceptibility masses compared to controls. Friedreich’s ataxia patients and, only on a trend level, SCA2 patients showed higher susceptibilities in more circumscribed parts of the dentate. In contrast, SCA6 patients revealed lower susceptibilities and susceptibility masses compared to controls throughout the dentate nucleus. SCA3 patients showed no significant changes in susceptibility and susceptibility mass. Lower volume of the dentate nuclei was found to varying degrees in all ataxia types. It was most pronounced in SCA6 patients and least prominent in SCA3 patients. The findings show that alterations in susceptibility revealed by quantitative susceptibiltiy mapping are common in the dentate nuclei in different types of cerebellar ataxias. The most striking changes in susceptibility were found in SCA1, MSA-C, and SCA6. Because iron content is known to be high in glial cells but not in neurons of the cerebellar nuclei, the higher susceptibility in SCA1 and MSA-C may be explained by a reduction of neurons (increase in iron concentration) and/or an increase in iron-rich glial cells, e.g. microgliosis. Hypomyelination also leads to higher susceptibility and could also contribute. The lower susceptibility in SCA6 suggests a loss of iron-rich glial cells. Quantitative susceptibility maps warrant future studies of iron content and iron-rich cells in ataxias to gain a more comprehensive understanding of the pathogenesis of these diseases.

Background: COQ4 codes for a mitochondrial protein required for coenzyme Q 10 (CoQ 10 ) biosynthesis. Autosomal recessive COQ4associated CoQ 10 deficiency leads to an early-onset mitochondrial multi-organ disorder. Methods: In-house exome and genome datasets (n = 14,303) were screened for patients with bi-allelic variants in COQ4. Work-up included clinical characterization and functional studies in patient-derived cell lines.Results: Six different COQ4 variants, three of them novel, were identified in six adult patients from four different families. Three patients had a phenotype of hereditary spastic paraparesis, two sisters showed a predominant cerebellar ataxia, and one patient had mild signs of both. Studies in patient-derived fibroblast lines revealed significantly reduced amounts of COQ4 protein, decreased CoQ 10 concentrations, and elevated levels of the metabolic intermediate 6-demethoxyubiquinone. Conclusion:We report bi-allelic variants in COQ4 causing an adult-onset ataxia-spasticity spectrum phenotype and a disease course much milder than previously reported.

Most individuals with rare diseases initially consult their primary care physician. For a subset of rare diseases, efficient diagnostic pathways are available. However, ultra-rare diseases often require both expert clinical knowledge and comprehensive genetic diagnostics, which poses structural challenges for public healthcare systems. To address these challenges within Germany, a novel structured diagnostic concept, based on multidisciplinary expertise at established university hospital centers for rare diseases (CRDs), was evaluated in the three year prospective study TRANSLATE NAMSE. A key goal of TRANSLATE NAMSE was to assess the clinical value of exome sequencing (ES) in the ultra-rare disease population. The aims of the present study were to perform a systematic investigation of the phenotypic and molecular genetic data of TRANSLATE NAMSE patients who had undergone ES in order to determine the yield of both ultra-rare diagnoses and novel gene-disease associations; and determine whether the complementary use of machine learning and artificial intelligence (AI) tools improved diagnostic effectiveness and efficiency. ES was performed for 1,577 patients (268 adult and 1,309 pediatric). Molecular genetic diagnoses were established in 499 patients (74 adult and 425 pediatric). A total of 370 distinct molecular genetic causes were established. The majority of these concerned known disorders, most of which were ultra-rare. During the diagnostic process, 34 novel and 23 candidate genotype-phenotype associations were delineated, mainly in individuals with neurodevelopmental disorders. To determine the likelihood that ES will lead to a molecular diagnosis in a given patient, based on the respective clinical features only, we developed a statistical framework called YieldPred. The genetic data of a subcohort of 224 individuals that also gave consent to the computer-assisted analysis of their facial images were processed with the AI tool Prioritization of Exome Data by Image Analysis (PEDIA) and showed superior performance in variant prioritization. The present analyses demonstrated that the novel structured diagnostic concept facilitated the identification of ultra-rare genetic disorders and novel gene-disease associations on a national level and that the machine learning and AI tools improved diagnostic effectiveness and efficiency for ultra-rare genetic disorders.