Case Study

GRIN Variant Screening Utilizing Automated Electrophysiology

GRIN variant screening using automated electrophysiology enables precise assessment of gain- and loss-of-function variants in the NMDA receptor GRIN gene family. Sygnature Discovery’s high-throughput platform supports detailed functional analysis to accelerate NMDA receptor-targeted drug discovery.

 

Introduction

N-methyl-D-aspartate receptors (NMDARs) are ionotropic glutamate receptors composed of two glycine-binding GluN1 subunits (encoded by GRIN1 gene) in combination with two glutamate-binding GluN2 subunits (encoded by GRIN2A, GRIN2B, GRIN2C or GRIN2D genes).

Rare de novo variants of the GRIN genes have been associated with neurodevelopmental disorders (NDD) and epileptic encephalopathy. Functional and pharmacological analysis of these variants can be used to understand the variant properties and classify the GRIN variants as Gain of Function (GoF) or Loss of Function (LoF) based on the overall net actions of a protein. The classification of these variants from patients may provide diagnostic advantages and, together with precision medicine approaches, could enable the development of a personalised therapy.

Until now, the functional analysis of the GRIN variants has been restricted to the conventional manual patch-clamp and two electrode voltage clamp (TEVC) techniques, which suffer from low throughput. Here, using automated patch-clamp technology, we describe the functional and pharmacological characterization of two GRIN2B variants, E413G (LoF) and S810R (GoF), and compare the results generated by automated patch clamp using HEK cells (at Sygnature Discovery) to data generated using manual patch clamp and TEVC at the Emory lab (Myers et al., 2023; Platzeret al., 2017).

The aim of this work was to demonstrate the capability of the automated patch-clamp system to identify variants with the LoF and GoF classifications. In future, the establishment of a higher throughput assay will enable a faster evaluation of the GRIN variants, where multiple variants can be assessed simultaneously, while obtaining robust data.

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