bioRxivNeurosciencePreprint
Deciphering the mechanistic basis for the pathological effect of the Gαo E246K mutation in neurodevelopmental disorder
Isra SadiyaiD, Irina NekrasovaiD, Meirav Avital‐Shacham, Naomi van WijkiD, Keren ZohariD, Nir KalismaniD, Dina Schneidman‐DuhovnyiD, Ehud BanneiD, Andreea NissenkorniD, Lubov BlumkiniD, Michal Linial BiD, Mickey KosloffiD
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Abstract
Mutations in the GNAO1 gene, which encodes for Go, a major neuronal G protein, are associated with neurodevelopmental disorders, epilepsy, and movement disorders. We identified and characterized a spontaneous heterozygous GNAO1 E246K mutation in an Israeli female infant with complex developmental delays and substantial motor difficulties. This mutation has been reported in other cases as a prevalent pathogenic mutation in patients with motor dysfunction and a broad range of neurological outcomes. To investigate the molecular and functional consequences of the Go E246K mutation, we employed structural modeling and analysis, biochemical assays, mass spectrometry-based proteomics, and cellular functional assays. We show that this mutation does not affect nucleotide binding, nor basal or RGS- accelerated GTP hydrolysis. Despite the E246 position located within a predicted effector binding region, proteomics analysis did not identify any new cellular partners. Instead, we demonstrate that the E246K mutation disrupts the Go regulatory GTPase cycle by directly impairing G{beta}{gamma} dissociation. This impairment overrides the presence of wild-type Go, explaining the dominant effect of the severe neurogenetic phenotype in the heterozygous background. These findings establish a new molecular mechanism for a GNAO1 mutation with dominant-negative effects on the GTPase regulatory cycle. The insights gained from studying this mechanism of action provide a basis for developing specific and personalized therapeutic strategies based on the outcome of a missense mutation in GNAO1.
- organism: human
- assay: proteomics
- research focus: differential expression
- approach: statistical model
- approach: experimental assay
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