Functional Characterization of KCNH2 genetic variants, encoding hERG potassium channel, as a clinically-relevant information for type 2 LQTS syndrome

Introduction hERG potassium channels are essential for normal cardiac electrical activity. Mutations in the hERG gene (KCNH2) cause long QT syndrome, a disorder that predisposes individuals to life-threatening arrhythmias. More than 1000 mutations in hERG sequence are described in databases and most of them remains to be characterized. Objective The objective of the work is to determine the consequences of the variants identified in LQT2 patients on the expression and function of the hERG channel. Methods An optimized sequence hERG fused to pHluorin tag was synthesized. By amplifying mutated overlapping fragments, and using the Gibson assembly strategy, we constructed hERG-pHluorin plasmids with missense variations. CHO cells were transfected by electroporation with the Maxcyte system or Fugene method, and studied after 40 hours. Confocal images and flow cytometry analysis were used to evaluate the percentage of transfected cells and to quantify the membrane channel expression, through pH sensitive pHluorin tag. Conventional or automated patch clamp were used to evaluate the hERG activity. Results In the present work, we constructed hERG plasmids carrying 48 variations identified in patients in France. Higher efficiency of transfection was observed with electroporation as compared to the Fugene method. The activity of hERG-pHluorin was validated in manual patch-clamp, the pHluorin tag does not interfere with channel activity. Characterization of several mutations in conventional patch clamp showed difference in current amplitude. Automated patch-clamp data was compared to manual patch-clamp, as an initial validation for high throughput screening of all constructed mutations. Conclusion High-throughput characterization of KCNH2 genetic variants is relevant to discriminate mutants that affect hERG channel activity from variants with undetectable effects. This information will be indicated in a patient database, as a basis of personalized medicine.