Multiscale mechanobiology of right ventricular failure

摘要/摘要 计算建模方法很少应用于右心室，即使像左心室 心力衰竭(LVF)、右心功能衰竭(RVF)是多因素、多尺度的 发病率和死亡率。与LVF相比，RVF的研究较少，其重要结论是 存在针对RV的特异性治疗方法。计算多尺度建模提供了一个独特的机会来整合 功能障碍表现在多个尺度上：在细胞器水平上，线粒体、钙离子- 处理和肌丝功能；在组织水平上，心肌细胞坏死、凋亡、纤维化和毛细血管 稀疏；在器官一级，肥大和扩张；在机体一级，运动不耐受。 此外，计算建模非常适合回答这样一个问题：相对贡献是什么 从多个尺度的异常到RVF的整体表型？ 我们建议用数据驱动的、多尺度的计算建模方法来回答这个问题。 从适用于健康和RVF线粒体的现有线粒体动力学计算模型开始 功能，我们将在组织水平上预测功能障碍的出现。然后，安装一个心肌组织 健康和RVF被动和主动力学的计算模型，我们将预测出现 器官层面的功能障碍。最后，通过将现有的双室力学计算模型应用于 健康和RVF压力-容量动力学，我们将预测在生物体水平上出现功能障碍， 即运动不耐受。模型假设和预测将由实验驱动并根据实验进行测试 使用最先进的技术在细胞器、组织、器官和生物体的多个尺度上收集的数据 在已建立的RVF大鼠模型中的时间点。最后，我们将使用数据驱动的计算建模 用我们的现状来确认在啮齿动物中发现的裂谷热机制与人类疾病的相关性 人体衰竭和非衰竭心肌的ART实验技术。我们的具体目标是： 目的1：确定RVF收缩功能障碍的驱动因素。我们假设心脏收缩压的主要驱动力 RVF的功能障碍是线粒体ATP生成受损，导致心脏收缩受损 肌丝。我们将使用特定尺度的模型和收集的多尺度实验数据来检验这一假设 来自患有RVF的大鼠。 目的2：确定RVF患者舒张期功能障碍的驱动因素。我们假设舒张期功能不全 RVF是由纤维化和受损的肌丝松弛引起的。我们将使用特定于规模的工具来检验这一假设 收集RVF大鼠模型和多尺度实验数据。 目的3：确定人RVF收缩和舒张期功能的驱动因素。细胞器的关键预测- RVF的组织尺度结构和功能驱动因素将通过验证的多尺度建模进行测试 在非衰竭和衰竭的人类心脏组织中，在这些尺度上进行最先进的测量。

SUMMARY/ABSTRACT Computational modeling approaches are rarely applied to the right ventricle even though, like left ventricular failure (LVF), right ventricular failure (RVF) is multifactorial, multiscale and causes significant morbidity and mortality. In comparison to LVF, RVF is understudied with the important consequence that no RV-specific therapies exist. Computational multi-scale modeling offers a unique opportunity to integrate dysfunction manifest at multiple scales: at the organelle level, there are impairments of mitochondria, Ca2+- handling, and myofilament function; at the tissue level, there is myocyte necrosis, apoptosis, fibrosis and capillary rarefaction; at the organ level, hypertrophy and dilation; and at the organism level, exercise intolerance. Moreover, computational modeling is ideally suited to answering the question: what are the relative contributions from abnormalities at multiple scales to the overall phenotype of RVF? We propose to answer this question with a data-driven, multiscale, computational modeling approach. Beginning with an existing mitochondrial kinetic computational model fit to healthy and RVF mitochondrial function, we will predict the emergence of dysfunction at the tissue-level. Then, fitting a myocardial tissue computational model to healthy and RVF passive and active mechanics, we will predict emergence of dysfunction at the organ-level. Finally, by adapting an existing biventricular mechanics computational model to healthy and RVF pressure-volume dynamics, we will predict the emergence of dysfunction at the organism-level, i.e., exercise intolerance. Model assumptions and predictions will be driven-by and tested against experimental data collected using state-of-the-art techniques at the organelle-, tissue-, organ-, and organism-scales at multiple time points in an established rat model of RVF. Finally, we will use our data-driven computational modeling approach to confirm the human disease relevance of mechanisms of RVF found in rodent using our state-of-the- art experimental techniques on human failing and nonfailing myocardium. Our specific aims are: Aim 1: Determine the drivers of systolic dysfunction in RVF. We hypothesize that the major driver of systolic dysfunction in RVF is impaired mitochondrial generation of ATP leading to impaired contraction of cardiac myofilaments. We will test this hypothesis with scale-specific models and multi-scale experimental data collected from rats with RVF. Aim 2: Determine the drivers of diastolic dysfunction in RVF. We hypothesize that diastolic dysfunction in RVF is driven by fibrosis and impaired myofilament relaxation. We will test this hypothesis with scale-specific models and multi-scale experimental data collected from rats with RVF. Aim 3: Determine the drivers of systolic and diastolic function in human RVF. Key predictions of organelle- and tissue-scale structural and functional drivers of RVF will be tested with multiscale modeling validated with state-of-the-art measurements at these scales in non-failing and failing human heart tissues.