Juliane Winkelmann’s research while affiliated with Rechts der Isar Hospital and other places

Stuttering is a common neurodevelopmental condition characterized by disfluencies in speech, such as blocks, prolongations, and repetitions. While most children who stutter do so only transiently, there are some for whom stuttering persists into adulthood. Rare-variant screens in families including multiple relatives with persistent stuttering have so far identified six genes carrying putative pathogenic variants hypothesized to act in a monogenic fashion. Here, we applied a complementary study design, searching instead for de novo variants in exomes of 85 independent parent-child trios, each with a child with transient or persistent stuttering. Exome sequencing analysis yielded a pathogenic variant in SPTBN1 as well as likely pathogenic variants in PRPF8, TRIO, and ZBTB7A - four genes previously implicated in neurodevelopmental disorders with or without speech problems. Our results also highlighted two further genes of interest for stuttering: FLT3 and IREB2. We used extensive bioinformatic approaches to investigate overlaps in brain-related processes among the twelve genes associated with monogenic forms of stuttering. Analyses of gene-expression datasets of the developing and adult human brain, and data from a genome-wide association study of human brain structural connectivity, did not find links of monogenic stuttering to specific brain processes. Overall, our results provide the first direct genetic link between stuttering and other neurodevelopmental disorders, including speech delay and aphasia. In addition, we systematically demonstrate a dissimilarity in biological pathways associated with the genes thus far implicated in monogenic forms of stuttering, indicating heterogeneity in the etiological basis of this condition.

MEIS1 and MEIS2 encode highly conserved homeodomain transcription factors crucial for developmental processes in a wide range of tissues, including the brain. They can execute redundant functions when co-expressed in the same cell types, but their roles during early stages of neural differentiation have not been systematically compared. By separate knockout and overexpression of MEIS1 and MEIS2 in human neural stem cells, we find they control specific sets of target genes, associated with distinct biological processes. Integration of DNA binding sites with differential transcriptomics implicates MEIS1 to co-regulate gene expression by interaction with transcription factors of the SOX and FOX families. MEIS1 harbors the strongest risk factor for restless legs syndrome (RLS). Our data suggest that MEIS1 can directly regulate the RLS-associated genes NTNG1, MDGA1 and DACH1, constituting new approaches to study the elusive pathomechanism or RLS. Supplementary Information The online version contains supplementary material available at 10.1038/s41598-024-80266-9.

Objectives: Normal sleep is crucial for brain health. Recent studies have reported robust associations between sleep disturbance and various brain structural and functional traits. However, the complex interplay between sleep health and macro-scale brain organization remains inconclusive. In this study, we aimed to uncover the links between brain imaging features and diverse sleep health-related characteristics by means of Machine Learning (ML). Methods: We used 28,088 participants from the UK Biobank to calculate 4677 structural and functional neuroimaging markers. Then, we employed them to predict self-reported insomnia symptoms, sleep duration, easiness getting up in the morning, chronotype, daily nap, daytime sleepiness, and snoring. We built seven different linear and nonlinear ML models for each sleep health-related characteristic to assess their predictability. Results: We performed an extensive ML analysis that involved more than 100,000 hours of computing. We observed relatively low performance in predicting all sleep health-related characteristics (e.g., balanced accuracy ranging between 0.50-0.59). Across all models, the best performance achieved was 0.59, using a Linear SVM to predict easiness getting up in the morning. Conclusions: The low capability of multimodal neuroimaging markers in predicting sleep health-related characteristics, even under extensive ML optimization in a large population sample suggests a complex relationship between sleep health and brain organization.

Objective De novo variants in cullin‐3 ubiquitin ligase ( CUL3 ) have been strongly associated with neurodevelopmental disorders (NDDs), but no large case series have been reported so far. Here, we aimed to collect sporadic cases carrying rare variants in CUL3 , describe the genotype–phenotype correlation, and investigate the underlying pathogenic mechanism. Methods Genetic data and detailed clinical records were collected via multicenter collaboration. Dysmorphic facial features were analyzed using GestaltMatcher. Variant effects on CUL3 protein stability were assessed using patient‐derived T‐cells. Results We assembled a cohort of 37 individuals with heterozygous CUL3 variants presenting a syndromic NDD characterized by intellectual disability with or without autistic features. Of these, 35 have loss‐of‐function (LoF) and 2 have missense variants. CUL3 LoF variants in patients may affect protein stability leading to perturbations in protein homeostasis, as evidenced by decreased ubiquitin‐protein conjugates in vitro. Notably, we show that 4E‐BP1 (EIF4EBP1), a prominent substrate of CUL3, fails to be targeted for proteasomal degradation in patient‐derived cells. Interpretation Our study further refines the clinical and mutational spectrum of CUL3 ‐associated NDDs, expands the spectrum of cullin RING E3 ligase‐associated neuropsychiatric disorders, and suggests haploinsufficiency via LoF variants is the predominant pathogenic mechanism. ANN NEUROL 2024

Background Despite considerable heritability, previous smaller genome‐wide association studies (GWASs) have not identified any robust genetic risk factors for isolated dystonia. Objective The objective of this study was to perform a large‐scale GWAS in a well‐characterized, multicenter sample of >6000 individuals to identify genetic risk factors for isolated dystonia. Methods Array‐based GWASs were performed on autosomes for 4303 dystonia participants and 2362 healthy control subjects of European ancestry with subgroup analysis based on age at onset, affected body regions, and a newly developed clinical score. Another 736 individuals were used for validation. Results This GWAS identified no common genome‐wide significant loci that could be replicated despite sufficient power to detect meaningful effects. Power analyses imply that the effects of individual variants are likely very small. Conclusions Moderate single‐nucleotide polymorphism–based heritability indicates that common variants do not contribute to isolated dystonia in this cohort. Sequence‐based GWASs (eg, by whole‐genome sequencing) might help to better understand the genetic basis. © 2024 The Author(s). Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Brain geometry impacts brain function. A quantitative encoding of form is provided by the Laplace-Beltrami operator’s spectrum of eigenvalues (LBS). We examined LBS genetics of 22 subcortical brain structures including cerebellum in 19,862 healthy White-British UK Biobank participants by multivariate GWAS (MOSTest) on the first 49 eigenvalues each. Controlling for surface and volume, we identified 80 unique variants (p<1/22*5E-8) influencing the shapes of one or several structures, with the highest yield (37 variants) for brain stem. The previously known influence of several of these loci on basic morphology, such as volume, is thus shown to also influence complex shape. Known associations of observed loci with blood pressure, neurodegeneration, alcohol consumption, and mental disorders hint at preclinical stages of these conditions potentially mediating the genetic effect on brain morphology. Significant correlations between LBS of several brain structures and the polygenic risks of hypertension, ischemic stroke and schizophrenia evince brain shapes as early biomarkers.