In-Silico Modeling of Glycosylation Modulation Dynamics in K+ Ion Channels and Cardiac Signaling

Transcription

1 In-Silico Modeling of Glycosylation Modulation Dynamics in K+ Ion Channels and Cardiac Signaling Dongping Du, Hui Yang Industrial and Management Systems Engineering Andrew R. Ednie, Eric S. Bennett Molecular Pharmacology and Physiology University of South Florida July 29,

2 Research Background Action Potential (AP): Net changes of transmembrane potentials Cardiac Contraction Ventricular cell K+ Schematic diagram of a cardiac cell AP 2

3 Cardiac Action Potential Cardiac arrhythmias and sudden cardiac death K+ Channels Stimulated 20 mv AP of ventricular cells 100 ms 3

4 Research Background Glycosylation: The enzymatic process that attaches glycans to proteins, lipids, or other organic molecules 4

5 Motivations Congenital Disorders of Glycosylation (CDG): High infant mortality rate Multi-system effects, e.g., diabetes, cardiac diseases Prevalence of cardiac involvement Unknown etiology of cardiomyopathy among young CDG patients Goals Understand the pathology of cardiac disease among CDG patients Develop pertinent therapeutic solutions 5

6 Gaps Gaps Little is known on how reduced glycosylation affects cardiac electrical signaling I K1 Joint K+ current I Kslow I Kur myocyte Limitations Study the changes at molecular levels, i.e., channel level Connect changes at one organizational level, e.g., ion channels, to another organizational level, cardiac cells. Objectives: Couple in-silico studies with the wealth of data from our electrophysiological experiments to model, mechanistically, how reduced sialylation affects K+ activity and cardiac electrical signaling. 6

7 Challenges Kv channels share similar gating kinetics Experimental decomposition is challenging Patch Clamp Experiment Joint AP K+ Current 0 Currents Nerbonne et. Al

8 K+ Channel Currents Current density of K+ currents at different clamp voltages (Bondarenko et. al. model) 50 Current Density (pa/pf) ik1 iks ikur ikss ikr ito Small Voltage (mv) 8

9 K+ Current Decomposition Bi-exponential decomposition of the K+ currents f t = A 1 e t/τ 1 + A 2 e t/τ 2 + A 3 30 K+ current (pa/pf) Membrane Potential = 30mV I K,sum I to I Kslow I Kss iksum ik1 iks ikur ikss ikr ito Time (ms) 9

10 Data K+ Channel Gating Kinetics K+ Closed K+ Channel K+ Open K+ Channel Inactivated K+ Channel Refractory Period Steady State Activation Steady State Inactivation 1 Activation Probability Activation Probability Voltage (mv) Voltage (mv) 10

11 Research Methodology Transient Outward K+ Current, I to I to = G Kto a 3 toi to (V E K ) where G Kto is the maximum whole cell conductance (ms/uf), a to and i to are the activation and inactivation. a to t = a to 0 a ss,to e τ ato + a ss,to i to t = i to 0 i ss,to e t τ ito + i ss,to t K+ current Time (ms) 11

12 Research Methodology Transient Outward K+ Current, I Kslow I Kslow = G Kslow a ur i ur (V E K ), where G Kslow is the maximum whole cell conductance (ms/uf), a ur and i ur are the activation and inactivation. a ur t = a ur 0 a ss e τ aur + a ur i ur t = i ur 0 i ss e t τ iur + i ur Transient Outward K+ Current, I Kss I Kss = G Kss a Kss V E K where G Kss is the maximum whole cell conductance (ms/uf), a ur are the activation and inactivation. a Kss t = a Kss 0 a ks e τ Kss + a ks t t 12

13 Results IKslow- Steady State Activation & Steady State Inactivation IKslow SS IKto SS Silico WT In-vitro WT Silico ST3Gal4 -/ In-vitro ST3Gal4 -/- IKslow SS Voltage (mv) Silico WT In-vitro WT Silico ST3Gal4 -/- In-vitro ST3Gal4 -/ Voltage (mv) 13

14 Results IKto- Steady State Activation & Steady State Inactivation IKto SS SS Silico WT In-vitro WT Silico ST3Gal4 -/- In-vitro ST3Gal4 -/- IKto SS Voltage (mv) Voltage (mv) 14

15 Results IKslow (pa/pf) In-silico WT In-silico ST3Gal4 -/- IKto (pa/pf) In-silico WT In-silico ST3Gal4 -/ Time (ms) In-silico WT In-silico ST3Gal4 -/ Time (ms) 20 0 In-silico WT In-silico ST3Gal4 -/- IKss 4 2 AP (mv) Time (ms) Time (ms) 15

16 Conclusions Challenges Similar gating kinetics Multiple K+ currents vs a joint K+ current Decomposition of K+ currents Methodological and Biomedical Merits Glycosylation models of K+ currents Integrated glycosylation model of K+ channels and the ventricular cells. Improve fundamental knowledge about the functional role of sialylation in cardiac electrical signaling New pharmaceutical designs to correct aberrant glycosylation 16

17 Acknowledges CMMI IIP IOS Publications 1. D. Du, H. Yang, A. R. Ednie, and E. S. Bennett, Statistical Metamodeling and Sequential Design of Computer Experiments to Model Glyco-altered Gating of Sodium Channels in Cardiac Myocytes IEEE Journal of Biomedical and Health Informatics, second round review. 2. D. Du, H. Yang, S. Norring, and E. S. Bennett, In-silico modeling of glycosylation modulation dynamics in herg channels and cardiac electrical signaling, IEEE Journal of Biomedical and Health Informatics, vol. 18, issue 1, p , (Feature article by IEEE Journal of Biomedical and Health Informatics) 3. D. Du, H. Yang, S. Norring, and E. Bennett, "Multiscale modeling of glycosylation modulation dynamics in cardiac electrical signaling," Proceedings of 2011 IEEE Engineering in Medicine and Biology Society Conference (EMBC), pp , Aug Sept , Boston, MA. (Placed first in IBM Best Student Paper competition) 17

18 Thank you! Questions? 18