A Gene Replacement Humanization Platform for Rapid Functional Testing of Clinical Variants in Epilepsy-associated STXBP1

Abstract

AbstractPurposeFunctional evidence is a pillar of variant interpretation according to ACMG guidelines. Functional evidence can be obtained in a variety of models and assay systems, including patient-derived tissues and iPSCs, in vitro cellular assays, and in vivo assays. Here we evaluate the reliability and practicality of variant interpretation in the small animal model, C. elegans, through a series of experiments evaluating the function of syntaxin binding protein, STXBP1, a well-known causative gene for Early infantile epileptic encephalopathy 1 (EIEE1).MethodsUsing CRISPR, we replaced the coding sequence for unc-18 with the coding sequence for the human ortholog STXBP1. Next, we used CRISPR to introduce precise point mutations in the human STXBP1 coding sequence, reflecting three clinical categories (benign, pathogenic, and variants of uncertain significance (VUS)). We quantified 26 features of the resulting worms’ movement to train Random Forest (RF) and Support Vector Machines (SVM) machine learning classifiers on known pathogenic and benign variants. We characterized the classifiers, and then used the behavioral data from the VUS-expressing animals to predict the categorization of the VUS.ResultsWhereas knock-out worms without unc-18 are severely impaired in motor function, worms expressing STXBP1 in its place have restored motor function. We produced worms with STXBP1 variants previously classified by ACMG criteria, including 25 benign variants, 32 pathogenic, and 24 variants of uncertain significance (VUS). Using either SVM or RF classifiers, we were able to obtain a sensitivity of 0.84-0.97 on known benign and pathogenic strains. By comparing multiple ML classification methods, we were able to classify 9 of the VUS as functionally abnormal, suggesting that these VUS are likely to be pathogenic.ConclusionsWe demonstrate that automated analysis of a small animal system is an effective, scalable, and fast way to understand functional consequences of variants in STXBP1, one of the most common causes of genetic epilepsies and neurodevelopmental disorders.