RESUMO
Efforts to resolve the functional impact of variants of uncertain significance (VUS) have lagged behind the identification of new VUS; as such, there is a critical need for scalable VUS resolution technologies. Computational variant effect predictors (VEPs), once trained, can predict pathogenicity for all missense variants in a gene, set of genes, or the exome. Existing tools have employed information on known pathogenic and benign variants throughout the genome to predict pathogenicity of VUS. We hypothesize that taking a gene-specific approach will improve pathogenicity prediction over globally-trained VEPs. We tested this hypothesis using the gene TSC2, whose loss of function results in tuberous sclerosis, a multisystem mTORopathy affecting about 1 in 6,000 individuals born in the United States. TSC2 has been identified as a high-priority target for VUS resolution, with (1) well-characterized molecular and patient phenotypes associated with loss-of-function variants, and (2) more than 2,700 VUS already documented in ClinVar. We developed Tuberous sclerosis classifier to Resolve variants of Uncertain Significance in T SC2 (TRUST), a machine learning model to predict pathogenicity of TSC2 missense VUS. To test whether these predictions are accurate, we further introduce curated loci prime editing (cliPE) as an accessible strategy for performing scalable multiplexed assays of variant effect (MAVEs). Using cliPE, we tested the effects of more than 200 TSC2 variants, including 106 VUS. It is highly likely this functional data alone would be sufficient to reclassify 92 VUS with most being reclassified as likely benign. We found that TRUST's classifications were correlated with the functional data, providing additional validation for the in silico predictions. We provide our pathogenicity predictions and MAVE data to aid with VUS resolution. In the near future, we plan to host these data on a public website and deposit into relevant databases such as MAVEdb as a community resource. Ultimately, this study provides a framework to complete variant effect maps of TSC1 and TSC2 and adapt this approach to other mTORopathy genes.
RESUMO
Pathogenic loss-of-function SCN1A variants cause a spectrum of seizure disorders. We previously identified variants in individuals with SCN1A -related epilepsy that fall in or near a poison exon (PE) in SCN1A intron 20 (20N). We hypothesized these variants lead to increased PE inclusion, which introduces a premature stop codon, and, therefore, reduced abundance of the full-length SCN1A transcript and Na v 1.1 protein. We used a splicing reporter assay to interrogate PE inclusion in HEK293T cells. In addition, we used patient-specific induced pluripotent stem cells (iPSCs) differentiated into neurons to quantify 20N inclusion by long and short-read sequencing and Na v 1.1 abundance by western blot. We performed RNA-antisense purification with mass spectrometry to identify RNA-binding proteins (RBPs) that could account for the aberrant PE splicing. We demonstrate that variants in/near 20N lead to increased 20N inclusion by long-read sequencing or splicing reporter assay and decreased Na v 1.1 abundance. We also identified 28 RBPs that differentially interact with variant constructs compared to wild-type, including SRSF1 and HNRNPL. We propose a model whereby 20N variants disrupt RBP binding to splicing enhancers (SRSF1) and suppressors (HNRNPL), to favor PE inclusion. Overall, we demonstrate that SCN1A 20N variants cause haploinsufficiency and SCN1A -related epilepsies. This work provides insights into the complex control of RBP-mediated PE alternative splicing, with broader implications for PE discovery and identification of pathogenic PE variants in other genetic conditions.