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Schelhaas, H. J.

Publications and source records attributed to Schelhaas, H. J..

3 recordsLinked to original sources

CHD2 Dosage Ties Autolysosomal Pathway to Cortical Maturation in Disease and Evolution

The mechanisms linking evolutionary changes in gene regulation to brain development and neurodevelopmental disease susceptibility remain poorly understood. Here, we identify a human-specific variant in an enhancer region that reduces expression of the chromatin remodeler CHD2. We investigate the variant's functional consequences using genome editing, cross-primate induced pluripotent stem cell models, cortical organoids, single-cell transcriptomics, patient-derived cells, and neuronal network analyses. We demonstrate that CHD2 dosage bidirectionally regulates lysosomal function and autophagosome flux to set the tempo of neuronal maturation. Higher CHD2 expression, as found in ancestralized and non-human primate models, enhances lysosomal degradative capacity and accelerates dendritic and synaptic maturation. Conversely, CHD2 haploinsufficiency yields reciprocal defects and disrupts broader neurodevelopmental transcriptional programs. Restoring lysosomal function genetically or pharmacologically rescues neuronal maturation in CHD2-haploinsufficient neurons, establishing lysosomal dysfunction as a causal and therapeutically tractable mechanism. These findings reveal that CHD2 and lysosomal homeostasis constitute a critical molecular axis regulating the pace of cortical development across evolution and disease.

neuroscience↗

SCN1A-deficient hiPSC-derived excitatory neuronal networks display mutation-specific phenotypes

Dravet syndrome is a severe epileptic encephalopathy, characterized by (febrile) seizures, behavioral problems and developmental delay. 80% of Dravet syndrome patients have a mutation in SCN1A, encoding NaV1.1. Milder clinical phenotypes, such as GEFS+ (generalized epilepsy with febrile seizures plus), can also arise from SCN1A mutations. Predicting the clinical phenotypic outcome based on the type of mutation remains challenging, even when the same mutation is inherited within one family. Both this clinical and genetic heterogeneity add to the difficulties of predicting disease progression and tailored prescription of anti-seizure medication. A better understanding of the neuropathology of different SCN1A mutations, might give insight in differentiating the expected clinical phenotype and best fit treatment choice. Initially it was recognized that loss of Na+ -current in inhibitory neurons specifically resulted in disinhibition and consequently seizure generation. However, the extent to which excitatory neurons contribute to the pathophysiology is currently debated, and might depend on the patient clinical phenotype or the specific mutation in SCN1A. To examine the genotype-phenotype correlations of SCN1A mutations in relation to excitatory neurons, we investigated a panel of patient-derived excitatory neuronal networks differentiated on multi-electrode arrays. We included patients with different clinical phenotypes, harboring different mutations in SCN1A, plus a family where the same mutation leads to both GEFS+ and Dravet syndrome. We hitherto describe a previously unidentified functional excitatory neuronal network phenotype in the context of epilepsy, which corresponded to seizurogenic network prediction patterns elicited by proconvulsive compounds. We find that excitatory neuronal networks were differently affected, dependent on the type of SCN1A mutation, but not on clinical severity. Specifically, pore domain mutations could be distinguished from voltage sensing domain mutations. Furthermore, all patients showed aggravated neuronal network responses upon febrile temperatures. While the basal neuronal network phenotypes could not be distinguished based on patient clinical severity, retrospective drug screening revealed that anti-seizure medication only affected GEFS+ patient-, but not Dravet patient-derived neuronal networks in a patient specific and clinically relevant manner. In conclusion, our results indicate a mutation-specific excitatory neuronal network phenotype, which recapitulates the foremost clinically relevant features, providing future opportunities for precision therapies. HighlightsO_LIHuman stem cell derived excitatory neurons are affected by mutations in SCN1A and display mutation-specific, but not clinical phenotype specific, neuronal network phenotypes C_LIO_LIThe neuronal network phenotype we describe corresponds to seizurogenic network prediction patterns in vitro C_LIO_LIExcitatory neuronal networks respond to Dravet syndrome clinically relevant triggers, like febrile temperatures and Dravet-contraindicated ASM Carbamazepine C_LIO_LIRetrospective drug screening revealed that GEFS+ neuronal networks, but not Dravet neuronal networks respond to ASM in a patient-specific and clinical relevant manner C_LI

neuroscience↗

Characterization of Glycosylphosphatidylinositol Biosynthesis Defects by Clinical Features, Flow Cytometry, and Automated Image Analysis

BackgroundGlycosylphosphatidylinositol Biosynthesis Defects (GPIBDs) cause a group of phenotypically overlapping recessive syndromes with intellectual disability, for which pathogenic mutations have been described in 16 genes of the corresponding molecular pathway. An elevated serum activity of alkaline phosphatase (AP), a GPI-linked enzyme, has been used to assign GPIBDs to the phenotypic series of Hyperphosphatasia with Mental Retardation Syndrome (HPMRS) and to distinguish them from another subset of GPIBDs, termed Multiple Congenital Anomalies Hypotonia Seizures syndrome (MCAHS). However, the increasing number of individuals with a GPIBD shows that hyperphosphatasia is a variable feature that is not ideal for a clinical classification.\n\nMethodsWe studied the discriminatory power of multiple GPI-linked substrates that were assessed by flow cytometry in blood cells and fibroblasts of 39 and 14 individuals with a GPIBD, respectively. On the phenotypic level, we evaluated the frequency of occurrence of clinical symptoms and analyzed the performance of computer-assisted image analysis of the facial gestalt in 91 individuals.\n\nResultsWe found that certain malformations such as Morbus Hirschsprung and Diaphragmatic defects are more likely to be associated with particular gene defects (PIGV, PGAP3, PIGN). However, especially at the severe end of the clinical spectrum of HPMRS, there is a high phenotypic overlap with MCAHS. Elevation of AP has also been documented in some of the individuals with MCAHS, namely those with PIGA mutations. Although the impairment of GPI-linked substrates is supposed to play the key role in the pathophysiology of GPIBDs, we could not observe gene-specific profiles for flow cytometric markers or a correlation between their cell surface levels and the severity of the phenotype. In contrast, it was facial recognition software that achieved the highest accuracy in predicting the disease-causing gene in a GPIBD.\n\nConclusionsDue to the overlapping clinical spectrum of both, HPMRS and MCAHS, in the majority of affected individuals, the elevation of AP and the reduced surface levels of GPI-linked markers in both groups, a common classification as GPIBDs is recommended. The effectiveness of computer-assisted gestalt analysis for the correct gene inference in a GPIBD and probably beyond is remarkable and illustrates how the information contained in human faces is pivotal in the delineation of genetic entities.

genetics↗