Reduced protein stability of 11 pathogenic missense STXBP1/MUNC18-1 variants and improved disease prediction.

BACKGROUND

Pathogenic variants in STXBP1/Munc18-1 cause severe encephalopathies that are among the most common in genetic neurodevelopmental disorders. Different molecular disease mechanisms have been proposed and pathogenicity prediction is limited. This study aims to define a generalized disease concept for STXBP1-related disorders and improve prediction.

METHODS

A cohort of 11 disease-associated and five neutral variants (detected in healthy individuals) was tested in three cell-free assays, and in heterologous cells and primary neurons. Protein aggregation was tested using gel filtration and Triton-x-100 insolubility. A machine learning algorithm (PRESR) that uses both sequence- and 3D structure-based features was developed to improve pathogenicity prediction using 231 known disease-associated variants and comparison to our experimental data.

RESULTS

Disease-associated, but none of the neutral variants produced reduced protein levels. Cell-free assays demonstrated directly that disease-associated variants have reduced thermostability, with most variants denaturing around body temperature. In addition, most disease-associated variants impaired SNARE-mediated membrane fusion in a reconstituted assay. Aggregation/insolubility was observed for none of the variants in vitro or in neurons. PRESR outperformed existing tools substantially: Matthews correlation coefficient = 0.71 versus <0.55.

CONCLUSIONS

These data establish intrinsic protein instability as the generalizable, primary cause for STXBP1-related disorders and show that protein-specific ortholog and 3D information improves disease prediction. PRESR is a publicly available diagnostic tool (PRESR.russelllab.org).

Informations de copyright

Copyright © 2024. Published by Elsevier Inc.