Simulation Modeling of Reduced Glycosylation Effects on Potassium Channels of Mouse Cardiomyocytes

Abstract

Dilated cardiomyopathy (DCM) is the third most common cause of heart failure and the primary reason for heart transplantation; upward of 70% of DCM cases are considered idiopathic. Our in-vitro experiments showed that reduced hybrid/complex N-glycosylation in mouse cardiomyocytes is linked with DCM. Further, we observed direct effects of reduced N-glycosylation on K_v gating. However, it is difficult to rigorously determine the effects of glycosylation on K_v activity, because there are multiple K_v isoforms in cardiomyocytes contributing to the cardiac excitation. Due to complex functions of K_v isoforms, only the sum of K⁺ currents (I_Ksum) can be recorded experimentally and decomposed later using exponential fitting to estimate component currents, such as I_Kto, I_Kslow, and I_Kss. However, such estimation cannot adequately describe glycosylation effects and K_v mechanisms. Here, we propose a framework of simulation modeling of K_v kinetics in mouse ventricular myocytes and model calibration using the in-vitro data under normal and reduced glycosylation conditions through ablation of the Mgat1 gene (i.e., Mgat1KO). Calibrated models facilitate the prediction of K_v characteristics at different voltages that are not directly observed in the in-vitro experiments. A model calibration procedure is developed based on the genetic algorithm. Experimental results show that, in the Mgat1KO group, both I_Kto and I_Kslow densities are shown to be significantly reduced and the rate of I_Kslow inactivation is much slower. The proposed approach has strong potential to couple simulation models with experimental data for gaining a better understanding of glycosylation effects on K_v kinetics.

Keywords: N-glycosylation; dilated cardiomyopathy; genetic algorithm; potassium channel; simulation modeling.

Figure 1

Diagrams of major cardiac ion channels and their roles in the action potential. (A) Human ventricular myocytes. (B) Mouse ventricular myocytes.

Figure 2

(A) The action potential of mouse ventricular myocytes and underlying ioninc currents. (B) Predominant K⁺ currents in mouse cardiomyocytes and their shapes of whole-cell voltage clamp recordings.

Figure 3

In-vitro experimental results, adopted from Ednie et al. (2019b). (A) Averages and error bars of the peaks of I_Kto and I_Kslow, and steady-state amplitude of I_Kss. (B) Inactivation time constant of I_Kto and I_Kslow. Significant differences between WT and Mgat1KO at p ≤ 0.05 are indicated by an *. (n = 35 for WT and n = 38 for Mgat1KO).

Figure 4

Comparison of current traces between calibrated simulations models (gray line) and benchmark exponential fitting. (A) I_Kto for WT. (B) I_Kslow for WT. (C) I_Kto for Mgat1KO. (D) I_Kslow for Mgat1KO. Each plot includes 30 replications of simulations results.

Figure 5

Predicted current traces. (A) I_Kto for WT. (B) I_Kslow for WT. (C) I_Kto for Mgat1KO. (D) I_Kslow for Mgat1KO. Clamp voltages of 4.5 s were applied to from -50 to +50 mV by 10-mV increments from the holding potential -70 mV.

Figure 6

Predicted current density to voltage relationship under the WT and Mgat1KO conditions. (A) I_Kto. (B) I_Kslow. Voltage steps range from -60 to 50 mV by 10-mV increments.

Figure 7

Predicted inactivation time constants to voltage relationship under the WT and Mgat1KO conditions. (A) I_Kto. (B) I_Kslow. Voltage steps range from -60 to 50 mV by 10-mV increments.

Figure 8

Predicted steady-state inactivation to voltage relationship under the WT and Mgat1KO conditions. (A) I_Kto. (B) I_Kslow. Voltage steps range from -60 to 50 mV by 10-mV increments.

Figure 9

AP traces from WT and Mgat1KO. (A) Experimental data (Ednie et al., 2019b). (B) Model prediction. APs from the Mgat1KO group are significantly prolonged compared to WT in the repolarization phase.