The T-allele of a polymorphism (C825T) in the gene for the G-protein beta 3 subunit (GNB3) is associated with cardiovascular and metabolic disorders, distinct cellular features and altered drug responses. The molecular mechanisms that give rise to this complex phenotype have been linked to the occurrence of G beta 3s, a splice variant of GNB3. G beta 3s is predominantly expressed in cells with the 825T-allele. In the present study we describe the identification and characterization of an additional G beta 3 splice variant referred to as G beta 3s2. Its mRNA is expressed in heart, blood cells and tumour tissue, and its expression is also tightly associated with the GNB3 825T-allele. G beta 3s2 is generated by alternative splicing using non-canonical splice sites. G beta subunits belong to the family of propeller proteins and consist of seven regular propeller blades. Transcripts for G beta 3s2 are lacking 129 bp of the coding sequence of the wild-type G beta 3 protein. Thus the predicted structure consists of only six propeller blades, which resembles the structure of G beta 3s. Co-immunoprecipitation analyses indicated that G beta 3s2 dimerizes with different G gamma subunits, e.g. G gamma 5, G gamma 8(C) and G gamma 12. In Sf9 insect cells, expression of G beta 3s2 together with G gamma 12 enhances receptor-stimulated activation of G alpha(i2). Expression of G beta 3s2 in mammalian cells activated the mitogen-activated protein kinase cascade. Together, these results suggest that G beta 3s2 is a biologically active G beta variant which may play a role in the manifestation of the complex phenotype associated with the 825T-allele.

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Fast localization of earth faults in medium voltage grids is required in order to avoid subsequent faults and to quickly restore the normal grid operation. We propose a localization approach utilizing a signature database with high-resolution transient voltages. The database is created based on a digital twin of a live medium-voltage grid for which measurements of voltages during actual earth faults of known location are available. The robustness and accuracy of two different realizations of the signature based fault localization are investigated: (1) a comparison approach using a correlation metric; (2) a neural network that has been trained by the signatures provided by the digital twin. The performance of our approach is assessed based on artificially generated earth fault events as well as real field measurements from the electrical grid. CCS CONCEPTS• Hardware → Smart grid; • Computing methodologies → Simulation tools; Neural networks.