Quantifying the role of myocyte ultrastructure in atrial health and disease

项目总结：心房颤动（AF）是最常见的心律失常（影响约1-2%的 一般人群），导致生活质量明显下降，死亡率增加， 血流动力学改变、进行性心房和心室功能障碍以及栓塞性中风。许多疾病和 已知诸如心力衰竭的病症有助于导致AF的病理变化。 由于广泛的心房结构性房颤， 以及促进AF维持的电变化（“AF引起AF”）。发展急需的 房颤治疗的新策略取决于对细胞功能异常的理解 触发和维持心房组织中的心律失常。在细胞水平上，许多慢性炎症的标志性结构变化 心脏疾病是复杂的膜结构的退化， 细胞内Ca 2+释放和心肌收缩（EC偶联）-即，横小管（TT）结构， 其从细胞表面垂直投射到其内部，从而使整个EC偶联同步 牢房TT结构的退化通常与心律失常有关，但尚不清楚是否 TT损失是心律失常、代偿性适应不良或附带现象的直接原因。这甚至是 在心房中不太清楚，因为心房肌细胞表现出各种各样的TT结构，具有突出的轴向 小管此外，由分离心房肌细胞的过程诱导的TT降解（与完整心房肌细胞中更致密的TT相比， 组织）和在实验上使完整心脏组织去管的挑战迄今为止限制了 机械肌细胞和组织研究。因此，围绕亚细胞结构的作用的文献 AF中的（超微结构）重塑仍然是断裂的，目前我们对其在AF中的作用知之甚少。 有助于AF病理生理学。本提案的首要目标是区分变更的作用 在心房肌细胞超微结构从其他疾病相关后遗症结合详细的多层次 实验分析兔心房肌细胞和兔及人心房组织的广泛定量 多尺度计算模型该项目将开发和验证一套建模工具， 研究以下机制：（1）心房TT的自然发生变化影响EC偶联， 分离的心房肌细胞膜的稳定性;（2）TT组织中的组织梯度影响组织水平 电生理和EC耦合结果;（3）超微结构重构与离子重构协同作用 有利于心房心肌病的心房肌生成。我们认为，量化心房肌的作用， 超微结构的AF病理学可能揭示新的机制AF管理的见解。每个目标包括 严格生成和验证的建模框架，由心房肌细胞中的新实验提供信息， 组织，并测试特定的假设。模型和数据将通过软件自由广泛地分发 和数据库基础设施，由格兰迪博士的实验室和科学网络网站支持。

PROJECT SUMMARY: Atrial fibrillation (AF) is the most common cardiac arrhythmia (affecting ~1-2% of the general population), resulting in markedly reduced quality of life and increased mortality, due to a combination of altered hemodynamics, progressive atrial and ventricular dysfunction, and embolic stroke. Many diseases and conditions, like heart failure, are known to contribute to pathological changes leading to AF. Limitations in current therapy allow AF paroxysms to progress to persistent and chronic AF, as a result of extensive atrial structural and electrical changes that facilitate AF maintenance (“AF begets AF”). The development of urgently needed new strategies for AF treatment hinges upon improved understanding of how abnormalities in cellular function trigger and sustain arrhythmia in atrial tissue. At the cellular level, a hallmark structural change of many chronic cardiac diseases is degradation of the intricate membrane architecture that couples cardiac electrical excitation to intracellular Ca2+ release and myocardial contraction (EC coupling) – i.e., the transverse tubule (TT) structures, which project orthogonally from the cell surface to its interior and thereby synchronize EC coupling throughout the cell. Degradation of the TT architecture is generally associated with arrhythmia, but it is not yet clear whether TT loss is a direct contributor to arrhythmia, a compensatory maladaptation, or an epiphenomenon. This is even less clear in atria, as atrial myocytes exhibit a vastly variable range of TT architectures, with prominent axial tubules. Further, TT degradation induced by the process of isolating atrial myocytes (vs. denser TTs in intact tissues) and challenges in experimentally detubulating intact cardiac tissue has so far limited the design of mechanistic myocyte and tissue studies. As a result, the literature surrounding the role of subcellular structural (ultrastructural) remodeling in AF has remained fractured, and currently we know relatively little about its role in contributing to AF pathophysiology. The overarching goal of this proposal is to discriminate the role of changes in atrial myocyte ultrastructure from other disease-associated sequelae by combining detailed multi-level experimental analyses of rabbit atrial myocytes and rabbit and human atrial tissues with extensive quantitative multi-scale computational modeling. The project will develop and validate a suite of modeling tools used to investigate the mechanisms by which: (1) naturally occurring variations in atrial TTs influence EC coupling and membrane stability in isolated atrial myocytes; (2) tissue gradients in TT organization influence tissue-level electrophysiological and EC coupling outcomes; (3) ultrastructural remodeling synergizes with ionic remodeling to favor atrial arrhythmogenesis in atrial cardiomyopathy. We contend that quantifying the role of atrial ultrastructure in AF pathology may shed new mechanistic insight into AF management. Each aim includes rigorously generated and validated modeling frameworks, informed by novel experiments in atrial myocytes and tissues, and testing of specific hypotheses. Models and data will be distributed freely and widely via software and database infrastructure supported by Dr. Grandi's lab and scientific networking sites.