STAT1 gain-of-function (GoF) is an inborn error of immunity characterized by a common feature of chronic mucocutaneous candidiasis and variable additional clinical manifestations, including recurrent bacterial and viral infections, autoimmunity, lymphoproliferation, aneurysm formation, and bone fragility. Over 150 GoF mutations have been identified. However, the molecular mechanisms underlying individual STAT1 GoF mutations remain poorly understood. We developed an integrated approach combining computational prediction with functional characterization to validate STAT1 GoF variants and stratify variants based on their putative molecular mechanism toward a GoF. Single missense variants were screened in silico, and hits were analyzed using an in-house established semiautomatic molecular dynamics pipeline to predict STAT1 protein stability, activation dynamics, and DNA interaction.
Computational predictions were experimentally validated through stable expression of selected STAT1 variants in THP-1 monocytic cells. Functional characterization included analysis of STAT1 phosphorylation and dephosphorylation kinetics, together with transcriptomic profiling. Shared and mutation-specific characteristics were identified. Together, this integrated computational-experimental pipeline allows validation of STAT1 GoF variants and provides a framework for stratifying variants based on their putative molecular mechanism of action.
