Identification and Classification of Position-specific GABAA Receptor Subunit Missense Variants for Their Role In Hippocampal Pyramidal Neurons

Summary

This study introduces a multiscale framework, spanning from DNA to protein function and neural behavior. It presents a novel approach for investigating predicted pathogenic mutations in the GABA_A receptor subunit, hypothesizing that epileptogenic mutations and proximal mutations, predicted as pathogenic, may produce similar effects on the CA1 pyramidal neuron model.

Abstract

Understanding the effects of functionally unknown variants in epilepsy associated genes is crucial for elucidating disease pathophysiology and developing personalized therapeutics. With a multiscale framework, spanning from DNA sequence to protein function and neural behavior, we describe a novel approach for predicting and investigating pathogenic mutations, hypothesizing that epileptogenic mutations in the GABA_A receptor subunit and nearby predicted mutations may produce similar effects on the CA1 pyramidal neuron model. By exploring the characteristic relationships between predicted pathogenic mutations and proximal epileptogenic mutations, the study aims to estimate the effects of predicted mutations based on the effects of epileptogenic mutations on hippocampal pyramidal neuron simulations.

The methodology begins with the collection of GABA_A receptor γ2 subunit genetic data, followed by data cleaning and formatting performed in R using a custom script. Next, ensemble predictors will be applied to identify and prioritize the pathogenic missense variants of the γ2 subunit. Mapping a specific pathogenic variant (predicted) to the subunit structural domains shared by epileptogenic mutations will be illustrated, accompanied by molecular modeling of their effects and consideration of evolutionary conservation. Then, variant-specific meta-analysis and parameter normalization will be performed, followed by correlation analysis to identify any significant relationships between predicted mutations and proximal epileptogenic mutations. Using a Python-based neural simulator, multi-compartmental conductance-based neuron model, reflecting the effect of wild-type and epileptogenic mutants will be described. Simulation of neural responses generated by epileptogenic GABA_A receptor subtype will be considered for the rough estimation of the predicted pathogenic variants' effect on neural response. To our knowledge, this is the first protocol exploring a multiscale framework to estimate the effects of GABA_A receptor variants on neuronal behavior, crucial for epilepsy research. This protocol can serve as a foundation for enhancing predictions of cellular phenotypes caused by potentially pathogenic variants of GABA_A receptors associated with epilepsy.

Introduction

For nearly all human diseases, genetic variation plays a significant role in individual susceptibility. Therefore, understanding how sequence variations relate to disease risk offers a valuable way to uncover key processes involved in disease development and identify new approaches for prevention and treatment¹. This also applies to neurodevelopmental disorders, which rank among the most prevalent chronic medical conditions in pediatric primary care². Conditions such as autism spectrum disorder, intellectual disability, and epilepsy illustrate how genetic variation significantly influences individual susceptibility durin....

Protocol

1. In silico prediction o​f pathogenic variants

1. Variant data collection
   1. Using the ClinVar database²⁹, search for variants of uncertain significance (VUS) in the coding region of the gene of interest via the website: https://www.ncbi.nlm.nih.gov/clinvar/. Enter the gene symbol (e.g., GABRG2) in the search bar and filter the results to include only the desired types of variants, such as single-nucleotide, missense variants with uncertain significance. Download and save the data as data.xlxs (Supplementary File 4: Supplementary Table S1). Record the date of the download....

Results

This study utilizes a multiscale approach to predict and characterize the pathogenic variants in the γ2 subunit of the GABA_A receptor, a key component in the pathophysiology of epilepsy. Through the use of predictive models, molecular modeling, evolutionary conservation, structural examination, correlation analysis, and neural simulations, this approach enhances the classification of variants, with significant relevance for epilepsy research and possibly for clinical use. The o.......

Discussion

By applying a combination of computational genetics, molecular modeling and neural simulations, the approach presented in this paper has the potential to improve the classification of GABA_A receptor variants, offering valuable insights for both epilepsy research and clinical applications. A comprehensive analysis for the identification and prioritization of predicted pathogenic mutations is presented and extended into a framework that potentially bridges the gap between variant effects on protein and cellular .......

Materials

List of materials used in this article

Name	Company	Catalog Number	Comments
Brian2	Sorbonne Université, INSERM, CNRS, Institut de la Vision, France; Imperial College London, United Kingdom	2.8.0.4	Stimberg et al., 2019 (https://pypi.org/project/Brian2/ )
dbNSFP server	Genos Bioinformatics LLC, USA	v3.0	Liu et al., 2020 (http://database.liulab.science/dbNSFP) (https://sites.google.com/site/jpopgen/dbNSFP)
HOPE	Centre for Molecular and Biomolecular Informatics CMBI, Radboud University, Netherlands	1.1.1	Venselaar et al., 2010 (https://www3.cmbi.umcn.nl/hope/)
Jalview	University of Dundee, UK	JV2	Waterhouse et al., 2009 (https://www.jalview.org/)
Jupyter Notebook	Project Jupyter, USA		https://jupyter.org/install
Phyton	Python Software Foundation, USA	3.13	https://www.python.org/downloads/
Protter	ETH Zurich, Switzerland	Version 1.0	Omasits, et al., 2014 (https://wlab.ethz.ch/protter/start/)
R	The R Foundation for Statistical Computing, USA	R version 4.3.2	https://www.r-project.org/
RStudio	Posit software, PBC, USA	RStudio 2023.12.1+402 "Ocean Storm" Release	https://posit.co/downloads/