Ask about this productRelated genes to: GABRG2 Blocking Peptide
- Gene:
- GABRG2 NIH gene
- Name:
- gamma-aminobutyric acid type A receptor gamma2 subunit
- Previous symbol:
- -
- Synonyms:
- -
- Chromosome:
- 5q34
- Locus Type:
- gene with protein product
- Date approved:
- 1991-02-26
- Date modifiied:
- 2016-02-04
Related products to: GABRG2 Blocking Peptide
Related articles to: GABRG2 Blocking Peptide
- Certain events that occur in early life, such as changes in nutrition, can induce structural and functional modifications in brain development, leading to behavioral programing in the offspring. These effects depend on the timing, intensity, and duration of exposure, and may contribute to chronic disorders in adulthood. Artificial non-nutritive sweeteners (NNS), such as saccharin, have recently been proposed as potential developmental disruptors. Saccharin consumption during pregnancy is discouraged, as it can cross the placenta and accumulate in the fetus. - Source: PubMed
Publication date: 2026/04/30
Pacheco-Sánchez BeatrizLópez-Merchán RaquelRubio PabloGarcía-Martos PilarSuárez JuanSanjuan CarlosRubio LeticiaMartín-de-Las-Heras StellaRodríguez de Fonseca FernandoAlén Franciscode Ceglia MarialuisaRivera Patricia - This review examines how recent genetic and technological advances have transformed our understanding and treatment of genetic epilepsies (GEs), with a focus on disorders involving GABA receptors (GABRs) and the GABA transporter 1 (GAT-1) encoded by SLC6A1. About 1000 genes are associated with epilepsy, including ~100 directly linked to defined epilepsy syndromes. Many disease-causing variants affect ion channels and transporters, disrupting protein structure, trafficking, and synaptic function. These defects often underlie developmental and epileptic encephalopathies (DEEs). A key insight from recent studies is that endoplasmic reticulum (ER)-related pathology-such as protein misfolding, ER retention, and accelerated degradation, which are common consequences of those pathogenic variants. For example, mutations in SLC6A1 or GABRG2 lead to impaired trafficking and reduced surface expression of GAT-1 or GABR subunits, resulting in deficient inhibitory neurotransmission. These mechanisms have been validated using advanced cellular assays and mouse models, although such experimental approaches remain costly and labor-intensive. Artificial intelligence (AI) is emerging as a powerful complement to experimental studies. Computational approaches, including generative AI and protein language models, can predict mutation-induced changes in protein structure, stability, and interactions, aided by tools such as AlphaFold. These methods enable large-scale, system-level analysis of variants and hold promise for accelerating drug discovery. However, current AI models are limited by fragmented datasets and the inherent complexity of biological systems. Integrating AI with experimental research offers a scalable strategy to translate mechanistic insights across genetic epilepsies (GEs). For instance, 4-phenylbutyrate (PBA), tested in SLC6A1 and GABRG2 epilepsy mouse models and now in clinical trials (NCT04937062), shows promise for treating GEs and DEEs caused by ER-retained mutant proteins. AI-based prediction could help identify additional GEs likely to respond to similar therapeutic approaches. Overall, combining experimental and AI-driven methods represents a new frontier for advancing the diagnosis and treatment of GEs and DEEs. PLAIN LANGUAGE SUMMARY: Mutations in almost 1000 genes have been linked to epilepsies, including those affecting GABA signaling such as GABAA receptors and the GABA transporter. Using cell and mouse studies, we found that many of these gene mutations cause similar problems inside cells. Specifically, the mutant proteins get stuck inside the cell in a structure called the endoplasmic reticulum (ER) and cause ER stress. Importantly, an FDA-approved drug 4-phenylbutyrate (PBA) can reduce these problems. We propose using artificial intelligence (AI) to predict how different gene mutations affect protein function and to identify which patients are likely to benefit from PBA treatment. - Source: PubMed
Publication date: 2026/05/08
Wang JuexinKang Jing-Qiong - BackgroundAcetyl tributyl citrate (ATBC), an eco-friendly plasticizer, exhibits poorly characterized neurotoxic effects.ObjectiveWe integrated network toxicology, machine learning, and molecular docking to elucidate molecular mechanisms underlying the link between ATBC exposure and Alzheimer's disease (AD) pathogenesis.MethodsPotential action targets of ATBC were screened from ChEMBL, TargetNet, and SwissTarget Prediction databases; disease-associated targets were derived from differential expression analysis of GEO datasets. Overlapping candidates underwent protein-protein interaction network construction (STRING) and subsequent Gene Ontology (GO)/Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. Machine learning employing SHAP prioritized pivotal targets, while molecular docking and dynamics simulations validated binding affinities.ResultsWe identified 68 shared targets, of which five were designated as critical (CCKBR, RAF1, GABRG2, STS, RAPGEF3). GO enrichment revealed that ATBC compromises neuronal function and synaptic plasticity by perturbing glial cell differentiation, synaptic transmission, benzodiazepine receptor activity, and serine/threonine kinase activity. KEGG analysis implicated neuroactive ligand-receptor interactions, calcium, FoxO, and PI3K-Akt signaling pathways. Molecular simulations confirmed stable compound-target binding.ConclusionsThis integrative computational approach elucidates mechanisms underlying plasticizer-associated neurotoxicity in AD, establishing a framework for investigating neurological impacts of environmental contaminants. - Source: PubMed
Publication date: 2026/03/13
Peng TaoXu PeiliGuo XiaofangLin JianZhang MengfanLiu XinghuaYe JianglinLin Xingdong - - Source: PubMed
Publication date: 2026/03/09
Naveed MuhammadHanif NimraAziz TariqWaseem MuhammadAlharbi MetabAlshammari AbdulrahmanAlasmari Abdullah F - Epilepsy syndromes show marked clinical and genetic heterogeneity, with numerous functionally diverse genes involved in their etiology. Next-generation sequencing (NGS) has facilitated the identification of many monogenic epilepsy syndromes and enables earlier, more accurate diagnosis in pediatric patients. This study analyzes the molecular profiles of 87 pediatric patients with various forms of epilepsy in whom pathogenic or likely pathogenic variants were identified. Next-generation sequencing (NGS) using multi-gene epilepsy panels or whole-exome sequencing (WES) was performed. A total of 88 pathogenic or likely pathogenic variants were detected in 48 epilepsy-related genes; 30 variants occurred de novo. and were the most frequent contributors (12.6% and 9.2%, respectively). The highest percentage of positive diagnoses (48%) was observed in patients with developmental and epileptic encephalopathy (DEE), with variants identified in genes including , , , , , , , , , , , , , , , , and . Pathogenic variants in were found in four patients with KBG syndrome, while other genes appeared sporadically. Targeted massively parallel sequencing is an effective diagnostic tool for pediatric epilepsy. The presence of numerous single-case findings highlights the high genetic heterogeneity of epilepsy. This approach enabled more precise diagnoses that would not have been achieved through clinical evaluation alone, underscoring the importance of genetic testing for prognosis and treatment planning in pediatric patients with unexplained epilepsy. - Source: PubMed
Publication date: 2026/01/27
Chałupczyńska BeataCiara ElżbietaHalat-Wolska PaulinaPollak AgnieszkaStawiński PiotrJurkiewicz DorotaPiekutowska-Abramczuk DorotaGawlik MarzenaPietrasik JustynaCieślikowska AgataWicher DorotaUlatowska AgataJedlińska DominikaBorkowska JulitaChmielewski DariuszDunin-Wąsowicz DorotaKotulska-Jóźwiak KatarzynaChrzanowska KrystynaMadej-Pilarczyk Agnieszka