Multiscale cardiac modelling reveals the origins of notched T waves in long QT syndrome type 2

Nature Communications

Volumn 5, Issue , 2014, Pages

  Sadrieh, Arash ^a,b   Domanski, Luke ^c   Pitt Francis, Joe ^d   Mann, Stefan A ^a,b   Hodkinson, Emily C ^a,b   Ng, Chai Ann ^a,b   Perry, Matthew D ^a,b   Taylor, John A ^c   Gavaghan, David ^d   Subbiah, Rajesh N ^a,b   Vandenberg, Jamie I ^a,b   Hill, Adam P ^a,b  

^a VICTOR CHANG CARDIAC RESEARCH INSTITUTE   (Australia)

^b UNIVERSITY OF NEW SOUTH WALES   (Australia)

^c CSIRO   (Australia)

^d UNIVERSITY OF OXFORD   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

CARDIOVASCULAR DISEASE; CORRELATION; ELECTRICAL METHOD; GENOME; GERM CELL; HEALTH CARE; MOLECULAR ANALYSIS; POLARIZATION;

ARTICLE; CONTROLLED STUDY; ELECTRIC ACTIVITY; ELECTROCARDIOGRAM; GENE EXPRESSION; GENETIC VARIABILITY; HEART RATE; HEART REPOLARIZATION; HUMAN; HUMAN CELL; ION CURRENT; LONG QT SYNDROME; MAJOR CLINICAL STUDY; PHENOTYPE; QT INTERVAL; QT PROLONGATION; T WAVE; ELECTROCARDIOGRAPHY; GENETICS; GENOTYPE; HEART; PATHOPHYSIOLOGY;

ELECTROCARDIOGRAPHY, AMBULATORY; GENOTYPE; HEART; HUMANS; LONG QT SYNDROME;

EID: 84922432370     PISSN: None     EISSN: 20411723     Source Type: Journal    
DOI: 10.1038/ncomms6069     Document Type: Article

References (43)

1. Keating, M. T. & Sanguinetti, M. C. Molecular and cellular mechanisms of cardiac arrhythmias. Cell 104, 569-580 (2001).
2. Zhang, L. et al. Spectrum of ST-T-wave patterns and repolarization parameters in congenital long-QT Syndrome ECG findings identify genotypes. Circulation 102, 2849-2855 (2000).
3. Anderson, C. L. et al. Most LQT2 mutations reduce Kv11.1 (hERG) current by a class 2 (trafficking-deficient) mechanism. Circulation 113, 365-373 (2006).
4. Vincent, G. M., Timothy, K. W., Leppert, M. & Keating, M. The spectrum of symptoms and QT intervals in carriers of the gene for the long-QT syndrome. New Engl. J. Med. 327, 846-852 (1992).
5. Schwartz, P. J. Idiopathic long QT syndrome: progress and questions. Am. Heart J. 109, 399-411 (1985).
6. Malfatto, G., Beria, G., Sala, S., Bonazzi, O. & Schwartz, P. J. Quantitative analysis of T wave abnormalities and their prognostic implications in the idiopathic long QT syndrome. J.Am. Coll. Cardiol. 23, 296-301 (1994).
7. Lupoglazoff, J. M. et al. Notched T waves on Holter recordings enhance detection of patients with LQt2 (HERG) mutations. Circulation 103, 1095-1101 (2001).
8. Haarmark, C. et al. Reference values of electrocardiogram repolarization variables in a healthy population. J. Electrocardiol. 43, 31-39 (2010).
9. Bhuiyan, Z. A. et al. Recurrent intrauterine fetal loss due to near absence of HERG: clinical and functional characterization of a homozygous nonsense HERG Q1070X mutation. Heart Rhythm 5, 553-561 (2008).
10. Roden, D. M. Taking the 'idio' out of'idiosyncratic': predicting torsades de pointes. Pacing Clin. Electrophysiol. 21, 1029-1034 (1998).
11. Benhorin, J. et al. Variable expression of long QT syndrome among gene carriers from families with five different HERG mutations. Ann. Noninvasive. Electrocardiol. 7, 40-46 (2002).
12. Nelson, M. R. et al. An abundance of rare functional variants in 202 drug target genes sequenced in 14,002 people. Science 337, 100-104 (2012).
13. Bamshad, M. J. et al. Exome sequencing as a tool for Mendelian disease gene discovery. Nat. Rev. Genet. 12, 745-755 (2011).
14. Priori, S. G., Napolitano, C. & Schwartz, P. J. Low penetrance in the long-QT syndrome: clinical impact. Circulation 99, 529-533 (1999).
15. Hooks, D. A. et al. Laminar arrangement of ventricular myocytes influences electrical behavior of the heart. Circ. Res. 101, e103-e112 (2007).
16. Austin, T. M. et al. Modeling cardiac electrical activity at the cell and tissue levels. Ann. N.Y. Acad. Sci. 1080, 334-347 (2006).
17. Bailey, J. E. Lessons from metabolic engineering for functional genomics and drug discovery. Nat. Biotechnol. 17, 616-618 (1999).
18. Sadrieh, A. et al. Quantifying the origins of population variability in cardiac electrical activity through sensitivity analysis of the electrocardiogram. J. Physiol. (Lond) 591, 4207-4222 (2013).
19. Owens, J. D. et al. A survey of general-purpose computation on graphics hardware. Comput. Graph. Forum 26, 80-113 (2007).
20. DI Veroli, G. Y., Davies, M. R., Zhang, H., Abi-Gerges, N. & Boyett, M. R. hERG inhibitors with similar potency but different binding kinetics do not pose the same proarrhythmic risk: implications for drug safety assessment. J. Cardiovasc. Electrophysiol. 25, 197-207 (2013).
21. Brennan, T., Fink, M. & Rodriguez, B. Multiscale modelling of drug-induced effects on cardiac electrophysiological activity. Eur. J. Pharm. Sci. 36, 62-77 (2009) .
22. Romero, L., Trenor, B., Yang, P.-C., Saiz, J. & Clancy, C. E. In silico screening of the impact of hERG channel kinetic abnormalities on channel block and susceptibility to acquired long QT syndrome. J. Mol Cell. Cardiol. 72C, 126-137 (2014).
23. Gaborit, N. et al. Gender-related differences in ion-channel and transporter subunit expression in non-diseased human hearts. J. Mol. Cell Cardiol. 49, 639-646 (2010).
24. Attwell, D., Cohen, I., Eisner, D., Ohba, M. & Ojeda, C. The steady state TTX-sensitive ('window') sodium current in cardiac Purkinje fibres. Pflugers. Arch. 379, 137-142 (1979).
25. Sobie, E. A. Parameter sensitivity analysis in electrophysiological models using multivariable regression. Biophys. J. 96, 1264-1274 (2009).
26. Mann, S. A. et al. Epistatic effects of potassium channel variation on cardiac repolarization and atrial fibrillation risk. J. Am. Coll. Cardiol. 59, 1017-1025 (2012).
27. Lehmann, M. H. et al. T wave 'humps' as a potential electrocardiographic marker of the long QT syndrome. J. Am. Coll. Cardiol. 24, 746-754 (1994).
28. Sarkar, A. X., Christini, D. J. & Sobie, E. A. Exploiting mathematical models to illuminate electrophysiological variability between individuals. J. Physiol. (Lond) 590, 2555-2567 (2012).
29. Britton, O. J. et al. Experimentally calibrated population of models predicts and explains intersubject variability in cardiac cellular electrophysiology. Proc. Natl Acad. Sci. USA 110, E2098-E2105 (2013).
30. Mirams, G. R. et al. Chaste: an open source C ++ library for computational physiology and biology. PLoS Comput. Biol. 9, e1002970 (2013).
31. Gouvea de Barros, B., Sachetto Oliveira, R., Meira, W., Lobosco, M. & Weber dos Santos, R. Simulations of complex and microscopic models of cardiac electrophysiology powered by multi-GPU platforms. Comput. Math Methods Med. 2012, 824569 (2012).
32. Sato, D. et al. Acceleration of cardiac tissue simulation with graphic processing units. Med. Biol. Eng. Comput. 47, 1011-1015 (2009).
33. Yan, G. X. & Antzelevitch, C. Cellular basis for the normal T wave and the electrocardiographic manifestations of the long-QT syndrome. Circulation 98, 1928-1936 (1998).
34. Wilson, L. D., Jennings, M. M. & Rosenbaum, D. S. Point: M cells are present in the ventricular myocardium. Heart Rhythm 8, 930-933 (2011).
35. Janse, M. J., Coronel, R. & Opthof, T. Counterpoint: M cells do not have a functional role in the ventricular myocardium of the intact heart. Heart Rhythm 8, 934-937 (2011).
36. Couderc, J.-P. A unique digital electrocardiographic repository for the development of quantitative electrocardiography and cardiac safety: the Telemetric and Holter ECG Warehouse (THEW). J. Electrocardiol. 43, 595-600 (2010) .
37. O'hara, T., Virag, L., Varro, A. & Rudy, Y. Simulation of the undiseased human cardiac ventricular action potential: model formulation and experimental validation. PLoS Comput. Biol. 7, e1002061 (2011).
38. Clancy, C. E., Tateyama, M. & Kass, R. S. Insights into the molecular mechanisms of bradycardia-triggered arrhythmias in long QT-3 syndrome. J. Clin. Invest. 110, 1251-1262 (2002).
39. Yang, T. et al. Blocking Scn10a channels in heart reduces late sodium current and is antiarrhythmic. Circ. Res. 111, 322-332 (2012).
40. Wallman, M., Smith, N. P. & Rodriguez, B. Computational methods to reduce uncertainty in the estimation of cardiac conduction properties from electroanatomical recordings. Med. Image Anal. 18, 228-240 (2014).
41. Pathmanathan, P. et al. A numerical guide to the solution of the bi-domain equations of cardiac electrophysiology. Prog. Biophys. Mol. Biol. 102, 136-155 (2010).
42. Baher, A. et al. Short-term cardiac memory and mother rotor fibrillation. Am. J. Physiol. Heart Circ. Physiol. 292, H180-H189 (2006).
43. Shah, R. R. & Morganroth, J. ICH E14 Q & A (R1) document: perspectives on the updated recommendations on thorough QT studies. Br. J. Clin. Pharmacol. 75, 959-965 (2013).