Alders, Mariëlle

Rationale : Semaphorin 3A ( SEMA3A )-encoded semaphorin is a chemorepellent that disrupts neural patterning in the nervous and cardiac systems. In addition, SEMA3A has an amino acid motif that is analogous to hanatoxin, an inhibitor of voltage-gated K + channels. SEMA3A -knockout mice exhibit an abnormal ECG pattern and are prone to ventricular arrhythmias and sudden cardiac death. Objective : Our aim was to determine whether SEMA3A is a naturally occurring protein inhibitor of K v 4.3 (I to ) channels and its potential contribution to Brugada syndrome. Methods and Results : K v 4.3, Na v 1.5, Ca v 1.2, or K v 4.2 were coexpressed or perfused with SEMA3A in HEK293 cells, and electrophysiological properties were examined via whole-cell patch clamp technique. SEMA3A selectively altered K v 4.3 by significantly reducing peak current density without perturbing K v 4.3 cell surface protein expression. SEMA3A also reduced I to current density in cardiomyocytes derived from human-induced pluripotent stem cells. Disruption of a putative toxin binding domain on K v 4.3 was used to assess physical interactions between SEMA3A and K v 4.3. These findings in combination with coimmunoprecipitations of SEMA3A and K v 4.3 revealed a potential direct binding interaction between these proteins. Comprehensive mutational analysis of SEMA3A was performed on 198 unrelated SCN5A genotype–negative patients with Brugada syndrome, and 2 rare SEMA3A missense mutations were identified. The SEMA3A mutations disrupted SEMA3A’s ability to inhibit K v 4.3 channels, resulting in a significant gain of K v 4.3 current compared with wild-type SEMA3A. Conclusions : This study is the first to demonstrate SEMA3A as a naturally occurring protein that selectively inhibits K v 4.3 and SEMA3A as a possible Brugada syndrome susceptibility gene through a K v 4.3 gain-of-function mechanism.

Background— Hundreds of nonsynonymous single nucleotide variants (nsSNVs) have been identified in the 2 most common long-QT syndrome-susceptibility genes ( KCNQ1 and KCNH2 ). Unfortunately, an ≈3% background rate of rare KCNQ1 and KCNH2 nsSNVs amongst healthy individuals complicates the ability to distinguish rare pathogenic mutations from similarly rare yet presumably innocuous variants. Methods and Results— In this study, 4 tools [(1) conservation across species, (2) Grantham values, (3) sorting intolerant from tolerant, and (4) polymorphism phenotyping] were used to predict pathogenic or benign status for nsSNVs identified across 388 clinically definite long-QT syndrome cases and 1344 ostensibly healthy controls. From these data, estimated predictive values were determined for each tool independently, in concert with previously published protein topology–derived estimated predictive values, and synergistically when ≥3 tools were in agreement. Overall, all 4 tools displayed a statistically significant ability to distinguish between case-derived and control-derived nsSNVs in KCNQ1 , whereas each tool, except Grantham values, displayed a similar ability to differentiate KCNH2 nsSNVs. Collectively, when at least 3 of the 4 tools agreed on the pathogenic status of C-terminal nsSNVs located outside the KCNH2/Kv11.1 cyclic nucleotide−binding domain, the topology-specific estimated predictive value improved from 56% to 91%. Conclusions— Although in silico prediction tools should not be used to predict independently the pathogenicity of a novel, rare nSNV, our results support the potential clinical use of the synergistic utility of these tools to enhance the classification of nsSNVs, particularly for Kv11.1’s difficult to interpret C-terminal region.

Background— Genetic testing for long-QT syndrome (LQTS) has diagnostic, prognostic, and therapeutic implications. Hundreds of causative mutations in 12 known LQTS-susceptibility genes have been identified. Genetic testing that includes the 3 most commonly mutated genes is available clinically. Distinguishing pathogenic mutations from innocuous rare variants is critical to the interpretation of test results. We sought to quantify the value of mutation type and gene/protein region in determining the probability of pathogenicity for mutations. Methods and Results— Type, frequency, and location of mutations across KCNQ1 (LQT1), KCNH2 (LQT2), and SCN5A (LQT3) were compared between 388 unrelated “definite” (clinical diagnostic score ≥4 and/or QTc ≥480 ms) cases of LQTS and >1300 healthy controls for each gene. From these data, estimated predictive values (percent of mutations found in definite cases that would cause LQTS) were determined according to mutation type and location. Mutations were 10 times more common in cases than controls (0.58 per case versus 0.06 per control). Missense mutations were the most common, accounting for 78%, 67%, and 89% of mutations in KCNQ1 , KCNH2 , and SCN5A in cases and >95% in controls. Nonmissense mutations have an estimated predictive value >99% regardless of location. In contrast, location appears to be critical for characterizing missense mutations. Relative frequency of missense mutations between cases and controls ranged from ≈1:1 in the SCN5A interdomain linker to infinity in the pore, transmembrane, and linker in KCNH2. These correspond to estimated predictive values ranging from 0% in the interdomain linker of SCN5A to 100% in the transmembrane/linker/pore regions of KCNH2. The estimated predictive value is also high in the linker, pore, transmembrane, and C terminus of KCNQ1 and the transmembrane/linker of SCN5A. Conclusions— Distinguishing pathogenic mutations from rare variants is of critical importance in the interpretation of genetic testing in LQTS. Mutation type, mutation location, and ethnic-specific background rates are critical factors in predicting the pathogenicity of novel mutations. Novel mutations in low-estimated predictive value regions such as the interdomain linker of SCN5A should be viewed as variants of uncertain significance and prompt further investigation to clarify the likelihood of disease causation. However, mutations in regions such as the transmembrane, linker, and pore of KCNQ1 and KCNH2 may be defined confidently as high-probability LQTS-causing mutations. These findings will have implications for other genetic disorders involving mutational analysis.