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Published on: March 12, 2013
Characterization of the phenotype and functional alternations of three HCN1 variants in Chinese epilepsy patients
Ziyao Han1, Lingling Xie1, Xiaorui Liu1
1Department of Neurology Children's Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Child Neurodevelopment and Cognitive Disorders, Chongqing 400014, China.
Purpose:
To evaluate the electrophysiological properties of three HCN1 variant sites found in Chinese epileptic patients and to explore the potential relationship between genotype and phenotype.
Methods:
We correlated clinical severity of three patients with HCN1 variants with whole-cell patch-clamp measurements of channel activity, channel expression detected by Western blot, and bioinformatics prediction of the damaging effects of each variant.
Results:
Three patients with the variants p.L400P, p.D534H and p.M243delinsTL, showed different phenotypes, ranging from mild epilepsy to severe epileptic encephalopathy. Variants L400P and D534H were classified as pathogenic by all bioinformatics tools, and variant M243delinsTL was classified as a polymorphism by MutationTaster. The L400P and D534H variants showed significantly reduced current compared with that of the wild-type (WT), while the current density of M243delinsTL was similar to WT. The half-activation voltage (V1/2) of M243delinsTL variant was shifted in the hyperpolarizing direction when compared to the WT, and the slope factor (k) of activation of the M243delinsTL variant was significantly lower than that of the WT. The L400P variant was associated with a significantly higher activation time constant compared with that of the WT. In addition, quantitative detection of the FLAG-tagged HCN1 channel revealed that the expression level of the L400P variant was significantly decreased compared to WT.
Conclusions:
Elucidation of the type and location of variant sites combined with the use of bioinformatics tools and patch-clamp techniques can improve our understanding of the clinical phenotype of epilepsy associated with HCN1 variants.
