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The American Journal of Human GeneticsGeneticsPeer-reviewed journal article

Variability in proliferative and migratory defects in Hirschsprung disease-associated RET pathogenic variants

Lauren E. FriesiD, Sree Dharma, Aravinda ChakravartiiD, Sumantra ChatterjeeiD

PDFDOIPreprint

Abstract

Despite the extensive genetic heterogeneity of Hirschsprung disease (HSCR; congenital colonic aganglionosis) 72% of patients harbor pathogenic variants in 10 genes that form a gene regulatory network (GRN) controlling the development of the enteric nervous system (ENS). Among these genes, the receptor tyrosine kinase gene RET is the most significant contributor, accounting for pathogenic variants in 12%-50% of patients depending on phenotype. RET plays a critical role in the proliferation and migration of ENS precursors, and defects in these processes lead to HSCR. However, despite the genes importance in HSCR, the functional consequences of RET pathogenic variants and their mechanism of disease remain poorly understood. To address this, we investigated the proliferative and migratory phenotypes in a RET-dependent neural crest-derived cell line harboring one of five missense (L56M, E178Q, Y791F, S922Y, F998L) or three nonsense (Y204X, R770X, Y981X) pathogenic heterozygous variants. Using a combination of cDNA-based and CRISPR-based PRIME editing coupled with quantitative proliferation and migration assays, we detected significant losses in cell proliferation and migration in three missense (E178Q, S922Y, F998L) and all nonsense variants. Our data suggests that the Y791F variant, whose pathogenicity has been debated, is likely not pathogenic. Importantly, the severity of migration loss did not consistently correlate with proliferation defects, and the phenotypic severity of nonsense variants was independent of their position within the RET protein. This study highlights the necessity and feasibility of targeted functional assays to accurately assess the pathogenicity of HSCR-associated variants, rather than relying solely on machine learning predictions, which could themselves be refined by incorporating such functional data.

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