Elliott, P. M. | Search bioRxiv

bioRxiv2026

Damaging RBM20 E-rich domain variants are not rescued by gene replacement

The promise of precision therapeutics in genetic cardiomyopathies relies on linking specific therapies to variant mechanisms. Missense variants in the cardiac splice regulator RBM20 cause a highly penetrant and arrhythmogenic dilated cardiomyopathy. Disease-causing variants in RBM20s arginine-serine rich (RS) domain act via formation of toxic gain of function cytoplasmic granules, but this is not true for a small number of clinically adjudicated pathogenic variants in its glutamate(E)-rich domain. To better define the effects of E-rich domain variants, we developed a scalable screen based on induced pluripotent stem cell (iPSC) cardiomyocyte differentiation that identified several additional damaging variants. Several of these reduced RBM20 protein abundance and stability. We therefore hypothesized that, unlike RS domain variants, these E-rich variants might be rescued by RBM20 overexpression. To test this hypothesis, we generated induced pluripotent stem cells (iPSCs) from a patient with a pathogenic E-rich domain variant (p.E913K), and confirmed reduced RBM20 protein expression in these RBM20+/p.E913K cells after differentiation to iPSC-derived cardiomyocytes (iPSC-CM, vs. engineered isogenic RBM20+/+). These iPSC-CMs also displayed aberrant transcriptional splicing, reduced contractility, increased calcium-induced calcium release, and nuclear localization of RBM20 protein, often with more than the two expected RBM20-centric splice factories. AAV-based overexpression of RBM20 reversed some, but not all of the mis-splicing events identified in RBM20+/p.E913K iPSC-CMs, and did not improve their abnormal contractility, calcium handling or supernumerary RBM20 nuclear granules. In summary, our data indicate that pathogenic E-rich domain variants reduce RBM20 protein abundance, but that their mechanism is unlikely to be explained by haploinsufficiency alone.

genetics↗

bioRxiv2023

Unlocking Predictive Power: A Machine Learning Tool Derived from In-Depth Analysis to Forecast the Impact of Missense Variants in Human Filamin C

Cardiomyopathies, diseases of the heart muscle, are a leading cause of heart failure. An increasing proportion of cardiomyopathies have been associated with specific genetic changes, such as mutations in FLNC, the gene that codes for filamin C. Altogether, more than 300 variants of FLNC have been identified in patients, including a number of single point mutations. However, the role of a significant number of these mutations remains unknown. Here, we conducted a comprehensive analysis, starting from clinical data that led to identification of new pathogenic and non-pathogenic FLNC variants. We selected some of these variants for further characterization that included studies of in vivo effects on the morphology of neonatal cardiomyocytes to establish links to phenotype, and the in vitro thermal stability and structure determination to understand biophysical factors impacting function. We used these findings to compile vast datasets of pathogenic and non-pathogenic variant structures and developed a machine-learning-based neural network (AMIVA-F) to predict the impact of single point mutations. AMIVA-F outperformed most commonly used predictors both in disease related as well as neutral variants, approaching [~]80% accuracy. Taken together, our study documents additional FLNC variants, their biophysical and structural properties, and their link to the disease phenotype. Furthermore, we developed a state-of-the-art web-based server AMIVA-F that can be used for accurate predictions regarding the effect of single point mutations in human filamin C, with broad implications for basic and clinical research.

biophysics↗