Physiological genomics identifies genetic modifiers of long QT syndrome type 2 severity
Sam Chai, … , Alfred L. George Jr., Isabelle Deschênes
Sam Chai, … , Alfred L. George Jr., Isabelle Deschênes
Published February 12, 2018
Citation Information: J Clin Invest. 2018;128(3):1043-1056. https://doi.org/10.1172/JCI94996.
View: Text | PDF
Research Article Cardiology Genetics

Physiological genomics identifies genetic modifiers of long QT syndrome type 2 severity

Abstract

Congenital long QT syndrome (LQTS) is an inherited channelopathy associated with life-threatening arrhythmias. LQTS type 2 (LQT2) is caused by mutations in KCNH2, which encodes the potassium channel hERG. We hypothesized that modifier genes are partly responsible for the variable phenotype severity observed in some LQT2 families. Here, we identified contributors to variable expressivity in an LQT2 family by using induced pluripotent stem cell–derived cardiomyocytes (iPSC-CMs) and whole exome sequencing in a synergistic manner. We found that iPSC-CMs recapitulated the clinical genotype-phenotype discordance in vitro. Importantly, iPSC-CMs derived from the severely affected LQT2 patients displayed prolonged action potentials compared with cells from mildly affected first-degree relatives. The iPSC-CMs derived from all patients with hERG R752W mutation displayed lower IKr amplitude. Interestingly, iPSC-CMs from severely affected mutation-positive individuals exhibited greater L-type Ca2+ current. Whole exome sequencing identified variants of KCNK17 and the GTP-binding protein REM2, providing biologically plausible explanations for this variable expressivity. Genome editing to correct a REM2 variant reversed the enhanced L-type Ca2+ current and prolonged action potential observed in iPSC-CMs from severely affected individuals. Thus, our findings showcase the power of combining complementary physiological and genomic analyses to identify genetic modifiers and potential therapeutic targets of a monogenic disorder. Furthermore, we propose that this strategy can be deployed to unravel myriad confounding pathologies displaying variable expressivity.

Authors

Sam Chai, Xiaoping Wan, Angelina Ramirez-Navarro, Paul J. Tesar, Elizabeth S. Kaufman, Eckhard Ficker, Alfred L. George Jr., Isabelle Deschênes

×

Figure 2

LQT2 genotype-phenotype discordance reproduced in patient-specific iPSC-CMs.

Options: View larger image (or click on image) Download as PowerPoint
LQT2 genotype-phenotype discordance reproduced in patient-specific iPSC-...
(A) Representative action potential traces from the control (IV-7), a severely-affected-phenotype LQT2 male (III-3), and his son, a hERG R752W mutant–positive, mildly-affected-phenotype male (IV-15). Summary APD90 (90% of repolarization) and APD50 (50% of repolarization) data are also shown (below traces). (B) Representative action potential traces from the control (IV-17) and the second pair (sister pair), a severely affected LQT2 female (IV-3) and a hERG R752W mutant–positive, mildly affected female (IV-4). Summary APD90 and APD50 data are shown (below traces). (C) Representative IKr traces and respective summary IKr tail current density from each patient-derived iPSC-CM depicted in A. (D) Representative IKr traces and respective summary IKr tail current density for each patient-derived iPSC-CM depicted in B. (E) A 1-Hz paced action potential train from IV-17 and IV-3 iPSC-CMs. Stars denote early afterdepolarizations. Dashed line in APD traces denotes 0 mV. Between 30 and 203 cells from 9 different iPSC clones (3 each from IV-17, III-3, IV-15 paired trio) were analyzed in A and C. Between 77 and 134 cells from 9 different iPSC clones (3 each from IV-17, IV-3, IV-4 paired trio) were analyzed in B and D. Exact numbers of replicate measures (n) for each are listed in Supplemental Table 3. Results are shown as mean ± SEM. *Statistical significance (P < 0.05) as determined by ANOVA in the summary data for A–D.