Neomorphic Gαo mutations gain interaction with Ric8 proteins in GNAO1 encephalopathies.

GNAO1 mutated in pediatric encephalopathies encodes the major neuronal G-protein Gαo. Of >80 pathogenic mutations, most are single amino acid substitutions spreading across Gαo sequence. We perform extensive characterization of Gαo mutants showing abnormal GTP uptake and hydrolysis, and deficiencies to bind Gßγ and RGS19. Plasma membrane localization of Gαo is decreased for a subset of mutations that leads to epilepsy; dominant interactions with GPCRs also emerge for the more severe mutants. Pathogenic mutants massively gain interaction with Ric8A and, surprisingly, Ric8B proteins, delocalizing them from cytoplasm to Golgi. Of these two mandatory Gα-subunit chaperones, Ric8A is normally responsible for the Gαi/o, Gαq, and Gα12/13 subfamilies, and Ric8B solely for Gαs/olf. Ric8A/B mediate the disease dominance when engaging in neomorphic interactions with pathogenic Gαo through disbalancing the neuronal G protein signaling networks. As the strength of Gαo-Ric8B interactions correlates with disease severity, our study further identifies an efficient biomarker and predictor for clinical manifestations in GNAO1 encephalopathies. Our work discovers the neomorphic molecular mechanism of mutations underlying pediatric encephalopathies and offers insights to other maladies caused by G protein misfunctioning and further genetic diseases.

Local and substrate-specific S-palmitoylation determines subcellular localization of Gαo.

Peripheral membrane proteins (PMPs) associate with cellular membranes through post-translational modifications like S-palmitoylation. The Golgi apparatus is generally viewed as the transitory station where palmitoyl acyltransferases (PATs) modify PMPs, which are then transported to their ultimate destinations such as the plasma membrane (PM). However, little substrate specificity among the many PATs has been determined. Here we describe the inherent partitioning of Gαo - α-subunit of heterotrimeric Go proteins - to PM and Golgi, independent from Golgi-to-PM transport. A minimal code within Gαo N-terminus governs its compartmentalization and re-coding produces G protein versions with shifted localization. We establish the S-palmitoylation at the outer nuclear membrane assay ("SwissKASH") to probe substrate specificity of PATs in intact cells. With this assay, we show that PATs localizing to different membrane compartments display remarkable substrate selectivity, which is the basis for PMP compartmentalization. Our findings uncover a mechanism governing protein localization and establish the basis for innovative drug discovery.

Predicting degradation rate of genipin cross-linked gelatin scaffolds with machine learning.

Genipin can improve weak mechanical properties and control high degradation rate of gelatin, as a cross-linker of gelatin which is widely used in tissue engineering. In this study, genipin cross-linked gelatin biodegradable porous scaffolds with different weight percentages of gelatin and genipin were prepared for tissue regeneration and measurement of their various properties including morphological characteristics, mechanical properties, swelling, degree of crosslinking and degradation rate. Results indicated that the sample containing the highest amount of gelatin and genipin had the highest degree of crosslinking and increasing the percentage of genipin from 0.125% to 0.5% enhances ultimate tensile strength (UTS) up to 113% and 92%, for samples with 2.5% and 10% gelatin, respectively. For these samples, increasing the percentage of genipin, reduce their degradation rate significantly with an average value of 124%. Furthermore, experimental data are used to develop a machine learning model, which compares artificial neural networks (ANN) and kernel ridge regression (KRR) to predict degradation rate of genipin-cross-linked gelatin scaffolds as a property of interest. The predicted degradation rate demonstrates that the ANN, with mean squared error (MSE) of 2.68%, outperforms the KRR with MSE = 4.78% in terms of accuracy. These results suggest that machine learning models offer an excellent prediction accuracy to estimate the degradation rate which will significantly help reducing experimental costs needed to carry out scaffold design.