GABA (A) receptors

GABA (A) 受体
  • 文章类型: Journal Article
    对大脑发育至关重要,神经发育和网络障碍,GABRA1基因编码α1亚基,异源五聚体γ-氨基丁酸A受体(GABAAR)的丰富和发育表达的亚基,介导大脑中的初级抑制。包括GABRA1基因在内的GABAAR亚基基因的突变与癫痫有关,一组综合症,以无缘无故的癫痫发作为特征,并通过综合方法诊断,这涉及基因测试。尽管有基因检测的诊断用途,包括GABRA1基因变体在内的大部分GABAAR亚基基因变体在其分子后果方面未知,精准和个性化医疗的挑战。解决这个问题,从ClinVar数据库中提取了137个未知临床意义的GABRA1基因变异,并对其进行了致病性计算分析.八个变体(L49H,P59L,W97R,D99G,G152S,V270G,T294R,P305L)被预测为致病性的,并定位到α1亚基的胞外域(ECD),跨膜结构域(TMD)和细胞外接头。随后是与从文献中检索到的癫痫综合征的细胞病理学和严重程度的相关数据的整合。我们的结果表明,GABRA1(L49H,P59L,W97R,D99G,G152S)可能会表现出轻度癫痫表型的表面表达减少和电流减少,而V270G,TMD中的T294R和第二和第三TMD之间的接头中的P305L将可能导致具有严重癫痫表型的细胞电流降低。这项研究的结果为临床遗传学和湿实验室实验提供了见解。
    Critical for brain development, neurodevelopmental and network disorders, the GABRA1 gene encodes for the α1 subunit, an abundantly and developmentally expressed subunit of heteropentameric gamma-aminobutyric acid A receptors (GABAARs) mediating primary inhibition in the brain. Mutations of the GABAAR subunit genes including GABRA1 gene are associated with epilepsy, a group of syndromes, characterized by unprovoked seizures and diagnosed by integrative approach, that involves genetic testing. Despite the diagnostic use of genetic testing, a large fraction of the GABAAR subunit gene variants including the variants of GABRA1 gene is not known in terms of their molecular consequence, a challenge for precision and personalized medicine. Addressing this, one hundred thirty-seven GABRA1 gene variants of unknown clinical significance have been extracted from the ClinVar database and computationally analyzed for pathogenicity. Eight variants (L49H, P59L, W97R, D99G, G152S, V270G, T294R, P305L) are predicted as pathogenic and mapped to the α1 subunit\'s extracellular domain (ECD), transmembrane domains (TMDs) and extracellular linker. This is followed by the integration with relevant data for cellular pathology and severity of the epilepsy syndromes retrieved from the literature. Our results suggest that the pathogenic variants in the ECD of GABRA1 (L49H, P59L, W97R, D99G, G152S) will probably manifest decreased surface expression and reduced current with mild epilepsy phenotypes while V270G, T294R in the TMDs and P305L in the linker between the second and the third TMDs will likely cause reduced cell current with severe epilepsy phenotypes. The results presented in this study provides insights for clinical genetics and wet lab experimentation.
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  • 文章类型: Review
    γ-氨基丁酸A型受体(GABA(A)受体)是由神经递质GABA激活的膜蛋白。在结构上,人类中枢神经系统中的这种主要抑制性神经递质受体是五聚体,可以由19个由α(1,2,3,4,5或6)组成的亚基组成,β(1,2或3),γ(1、2或3),ρ(1、2或3),和δ,π,θ,和ε。这产生了几种可能的五聚体排列,这也会影响受体的药理和生理特性。通过在几个亚基中添加短剪接变体和长剪接变体以及存在多个变构结合位点和可以与受体结合的扩张性配体,进一步增加了受体的复杂性和异质性。因此,需要对受体的结构和功能有全面的了解,才能对受体功能障碍的后果和随后的药物开发研究有新的见解。值得注意的是,计算辅助研究的进步促进了剩余相互作用的阐明和能量结合的探索,否则这可能是具有挑战性的调查。在这次审查中,我们的目的是总结从计算辅助应用的进展中获得的GABA(A)受体的结构和功能的当前理解。
    The γ-aminobutyric acid type A receptor (GABA (A) receptor) is a membrane protein activated by the neurotransmitter GABA. Structurally, this major inhibitory neurotransmitter receptor in the human central nervous system is a pentamer that can be built from a selection of 19 subunits consisting of α(1,2,3,4,5 or 6), β (1,2 or 3), γ (1,2 or 3), ρ (1,2 or 3), and δ, π, θ, and ε. This creates several possible pentameric arrangements, which also influence the pharmacological and physiological properties of the receptor. The complexity and heterogeneity of the receptors are further increased by the addition of short and long splice variants in several subunits and the existence of multiple allosteric binding sites and expansive ligands that can bind to the receptors. Therefore, a comprehensive understanding of the structure and function of the receptors is required to gain novel insights into the consequences of receptor dysfunction and subsequent drug development studies. Notably, advancements in computational-aided studies have facilitated the elucidation of residual interactions and exploring energy binding, which may otherwise be challenging to investigate. In this review, we aim to summarize the current understanding of the structure and function of GABA (A) receptors obtained from advancements in computational-aided applications.
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