Cyclic Nucleotide Regulation of Excitation-Contraction Coupling in Cardiac Myocytes

phosphodiesterase;β-adrenergic pathway;NO/cGMP/PKG pathway;cyclic nucleotide cross-talk signaling network;L-type calcium channel;cardiac myocytes;computational model.;Biomedical Engineering

Zhao, Claire Y.Tomaselli, Gordon F. ;

Johns Hopkins University

关键词: phosphodiesterase;    β-adrenergic pathway;    NO/cGMP/PKG pathway;    cyclic nucleotide cross-talk signaling network;    L-type calcium channel;    cardiac myocytes;    computational model.;    Biomedical Engineering;   

Others  :  https://jscholarship.library.jhu.edu/bitstream/handle/1774.2/40265/ZHAO-DISSERTATION-2016.pdf?sequence=1&isAllowed=y

瑞士|英语

来源: JOHNS HOPKINS DSpace Repository

【 摘 要 】

The balanced signaling between the two cyclic nucleotides (cNs) cAMP and cGMP in the cN cross-talk signaling network plays a critical role in regulating cardiac contractility. In particular, the signaling network imposes both stimulatory and inhibitory regulatory actions on L-type calcium (Ca2+) channel (LCC), which initiates Ca2+-induced Ca2+ release (CICR) and excitation-contraction (EC) coupling. Many therapeutic agents have been developed to selectively inhibit or stimulate proteins in the aforementioned pathway, in the attempt to manage and treat heart failure (HF). Nonetheless, it has been challenging to obtain a comprehensive systems-level understanding of the signal transduction mechanisms of the cN cross-talk signaling network, in part because of the participation of multiple phosphodiesterases (PDEs) in the common tasks of cN degradation, the complex interactions between the signaling proteins, and the large number of tightly-coupled EC coupling-related phosphorylation targets of the network. Accordingly, this work developed multi-scale, biophysically-detailed, and experimentally-validated computational models to study the cN cross-talk signaling network and its regulation of LCC. By precisely defining and quantifying biochemical reactions involved, this work bridged causal gaps between the characteristics of individual molecular components and the collective responses of the signaling network and its regulation of electrophysiology. Through predictive modeling and integrative analysis, this work provide insights into cellular mechanisms that are difficult to elucidate with experimentation alone and serves as solid foundation towards data-driven and simulation-guided medicine.