Identification of homologous GluN subunits variants accelerates GRIN variants stratification

Ana Santos-Gómez; Adrián García-Recio; Federico Miguez-Cabello; David Soto; Xavier Altafaj; Mireia Olivella

doi:10.3389/fncel.2022.998719

Identification of homologous GluN subunits variants accelerates GRIN variants stratification

Front Cell Neurosci. 2022 Dec 23:16:998719. doi: 10.3389/fncel.2022.998719. eCollection 2022.

Authors

Ana Santos-Gómez^{1

2}, Adrián García-Recio³, Federico Miguez-Cabello^{1

2}, David Soto^{1

2}, Xavier Altafaj^{1

2}, Mireia Olivella³

Affiliations

¹ Neurophysiology Laboratory, Department of Biomedicine, Faculty of Medicine and Health Sciences, Institute of Neurosciences, University of Barcelona, Barcelona, Spain.
² August Pi i Sunyer Biomedical Research Institute (IDIBAPS), Barcelona, Spain.
³ Bioinfomatics and Medical Statistics Group, University of Vic-Central University of Catalonia, Barcelona, Spain.

Abstract

The clinical spectrum of GRIN-related neurodevelopmental disorders (GRD) results from gene- and variant-dependent primary alterations of the NMDA receptor, disturbing glutamatergic neurotransmission. Despite GRIN gene variants' functional annotations being dually critical for stratification and precision medicine design, genetically diagnosed pathogenic GRIN variants currently outnumber their relative functional annotations. Based on high-resolution crystal 3D models and topological domains conservation between GluN1, GluN2A, and GluN2B subunits of the NMDAR, we have generated GluN1-GluN2A-GluN2B subunits structural superimposition model to find equivalent positions between GluN subunits. We have developed a GRIN structural algorithm that predicts functional changes in the equivalent structural positions in other GluN subunits. GRIN structural algorithm was computationally evaluated to the full GRIN missense variants repertoire, consisting of 4,525 variants. The analysis of this structure-based model revealed an absolute predictive power for GluN1, GluN2A, and GluN2B subunits, both in terms of pathogenicity-association (benign vs. pathogenic variants) and functional impact (loss-of-function, benign, gain-of-function). Further, we validated this computational algorithm experimentally, using an in silico library of GluN2B-equivalent GluN2A artificial variants, designed from pathogenic GluN2B variants. Thus, the implementation of the GRIN structural algorithm allows to computationally predict the pathogenicity and functional annotations of GRIN variants, resulting in the duplication of pathogenic GRIN variants assignment, reduction by 30% of GRIN variants with uncertain significance, and increase by 70% of functionally annotated GRIN variants. Finally, GRIN structural algorithm has been implemented into GRIN variants Database (http://lmc.uab.es/grindb), providing a computational tool that accelerates GRIN missense variants stratification, contributing to clinical therapeutic decisions for this neurodevelopmental disorder.

Keywords: GRIN; NMDA receptor; epilepsy; intellectual disability; mutation predictor.