PDF(Pubmed)
Abstract：
KCNMA1-linked channelopathy is a neurological disorder characterized by seizures, motor abnormalities, and neurodevelopmental disabilities. The disease mechanisms are predicted to result from alterations in KCNMA1-encoded BK K+ channel activity; however, only a subset of the patient-associated variants have been functionally studied. The localization of these variants within the tertiary structure or evaluation by pathogenicity algorithms has not been systematically assessed. In this study, 82 nonsynonymous patient-associated KCNMA1 variants were mapped within the BK channel protein. Fifty-three variants localized within cryoelectron microscopy-resolved structures, including 21 classified as either gain of function (GOF) or loss of function (LOF) in BK channel activity. Clusters of LOF variants were identified in the pore, the AC region (RCK1), and near the Ca2+ bowl (RCK2), overlapping with sites of pharmacological or endogenous modulation. However, no clustering was found for GOF variants. To further understand variants of uncertain significance (VUSs), assessments by multiple standard pathogenicity algorithms were compared, and new thresholds for sensitivity and specificity were established from confirmed GOF and LOF variants. An ensemble algorithm was constructed (KCNMA1 meta score (KMS)), consisting of a weighted summation of this trained dataset combined with a structural component derived from the Ca2+-bound and unbound BK channels. KMS assessment differed from the highest-performing individual algorithm (REVEL) at 10 VUS residues, and a subset were studied further by electrophysiology in HEK293 cells. M578T, E656A, and D965V (KMS+;REVEL-) were confirmed to alter BK channel properties in voltage-clamp recordings, and D800Y (KMS-;REVEL+) was assessed as benign under the test conditions. However, KMS failed to accurately assess K457E. These combined results reveal the distribution of potentially disease-causing KCNMA1 variants within BK channel functional domains and pathogenicity evaluation for VUSs, suggesting strategies for improving channel-level predictions in future studies by building on ensemble algorithms such as KMS.
摘要：
KCNMA1相关的信道病是一种以癫痫发作为特征的神经系统疾病，电机异常，和神经发育障碍。预测疾病机制是由KCNMA1编码的BKK通道活性的改变引起的；然而，仅对一部分患者相关变异进行了功能研究.尚未系统地评估这些变体在三级结构内的定位或通过致病性算法进行的评估。在这项研究中,在BK通道蛋白内定位了82种非同义患者相关的KCNMA1变体。位于cryo-EM分辨结构内的53个变体，包括21个在BK通道活动中被分类为功能获得(GOF)或功能丧失(LOF)。在孔中鉴定了LOF变体的簇，交流区域(RCK1)，和附近的Ca2+碗（RCK2），与药理学或内源性调节位点重叠。然而,未发现GOF变异的聚类.为了进一步理解不确定意义(VUS)的变体，比较了多种标准致病性算法的评估结果，并从证实的GOF和LOF变异中建立了新的敏感性和特异性阈值.构建了集成算法(KCNMA1MetaScore)，由这个训练的数据集的加权总和以及从Ca2+结合和未结合的BK通道导出的结构分量组成。KMS评估与10个VUS残基处性能最高的个体算法(REVEL)不同，通过电生理学在HEK293细胞中进一步研究了一个子集。M578T,E656A,和D965V(KMS+;REVEL-)被证实会改变电压钳记录中的BK通道特性，和D800Y(KMS-;REVEL+)在测试条件下被评估为良性的。然而,KMS未能准确评估K457E。这些综合结果揭示了BK通道功能域内潜在致病KCNMA1变体的分布和VUS的致病性评估，通过构建KMS等集成算法，提出在未来研究中改进信道级预测的策略。
