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

TITLE=Identification of homologous GluN subunits variants accelerates GRIN variants stratification

JOURNAL=Frontiers in Cellular Neuroscience

VOLUME=16

YEAR=2022

URL=https://www.frontiersin.org/journals/cellular-neuroscience/articles/10.3389/fncel.2022.998719

DOI=10.3389/fncel.2022.998719

ISSN=1662-5102

ABSTRACT=<p>The clinical spectrum of <italic>GRIN</italic>-related neurodevelopmental disorders (GRD) results from gene- and variant-dependent primary alterations of the NMDA receptor, disturbing glutamatergic neurotransmission. Despite <italic>GRIN</italic> gene variants’ functional annotations being dually critical for stratification and precision medicine design, genetically diagnosed pathogenic <italic>GRIN</italic> 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 <italic>GRIN</italic> structural algorithm that predicts functional changes in the equivalent structural positions in other GluN subunits. GRIN structural algorithm was computationally evaluated to the full <italic>GRIN</italic> 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 <italic>in silico</italic> 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 <italic>GRIN</italic> variants, resulting in the duplication of pathogenic <italic>GRIN</italic> variants assignment, reduction by 30% of <italic>GRIN</italic> variants with uncertain significance, and increase by 70% of functionally annotated <italic>GRIN</italic> variants. Finally, GRIN structural algorithm has been implemented into <italic>GRIN</italic> variants Database (<ext-link ext-link-type="uri" xlink:href="http://lmc.uab.es/grindb" xmlns:xlink="http://www.w3.org/1999/xlink">http://lmc.uab.es/grindb</ext-link>), providing a computational tool that accelerates <italic>GRIN</italic> missense variants stratification, contributing to clinical therapeutic decisions for this neurodevelopmental disorder.</p>