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Updated: Nov 26, 2025

Identification and Classification of Position-specific GABAA Receptor Subunit Missense Variants for Their Role In Hippocampal Pyramidal Neurons
Published on: June 6, 2025
Multi-parametric analysis of 57 SYNGAP1 variants reveal impacts on GTPase signaling, localization, and protein
Fabian Meili1, William J Wei2, Wun-Chey Sin1
1Djavad Mowafaghian Centre for Brain Health, University of British Columbia, Vancouver, BC V6T2B5, Canada.
Abstract:
SYNGAP1 is a neuronal Ras and Rap GTPase-activating protein with important roles in regulating excitatory synaptic plasticity. While many SYNGAP1 missense and nonsense mutations have been associated with intellectual disability, epilepsy, schizophrenia, and autism spectrum disorder (ASD), whether and how they contribute to individual disease phenotypes is often unknown. Here, we characterize 57 variants in seven assays that examine multiple aspects of SYNGAP1 function. Specifically, we used multiplex phospho-flow cytometry to measure variant impact on protein stability, pERK, pGSK3β, pp38, pCREB, and high-content imaging to examine subcellular localization. We find variants ranging from complete loss-of-function (LoF) to wild-type (WT)-like in their regulation of pERK and pGSK3β, while all variants retain at least partial ability to dephosphorylate pCREB. Interestingly, our assays reveal that a larger proportion of variants located within the disordered domain of unknown function (DUF) comprising the C-terminal half of SYNGAP1 exhibited higher LoF, compared to variants within the better studied catalytic domain. Moreover, we find protein instability to be a major contributor to dysfunction for only two missense variants, both located within the catalytic domain. Using high-content imaging, we find variants located within the C2 domain known to mediate membrane lipid interactions exhibit significantly larger cytoplasmic speckles than WT SYNGAP1. Moreover, this subcellular phenotype shows both correlation with altered catalytic activity and unique deviation from signaling assay results, highlighting multiple independent molecular mechanisms underlying variant dysfunction. Our multidimensional dataset allows clustering of variants based on functional phenotypes and provides high-confidence, multi-functional measures for making pathogenicity predictions.
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