Systems Mechanobiology Modeling for Patient-Specific Cardiac Fibrosis Predictions

项目总结 心脏纤维化是导致数百万心力衰竭患者舒张期和收缩期功能障碍的主要因素 病人。不幸的是，目前缺乏对心肌纤维化的预测和控制，部分原因是复杂性。 在胶原调节网络内，部分与患者之间的生化和 调节胶原蛋白周转的机械信号。我们最重要的假设是计算积分 多个生化和机械信号通路(而不是单一的生物标志物)将使 个性化的纤维化风险预测和改进的治疗选择。在前期工作中，我们开发了 跨越关键胶原调节过程的两个独特的大规模网络模型：心脏成纤维细胞 细胞内信号网络和细胞外胶原-基质金属蛋白酶-TIMP相互作用网络。对于建议的 工作中，我们将把细胞内和细胞外网络模型与新的细胞培养实验相结合， 现有的动物实验和现有的患者数据集，以测试该模型预测心脏疾病的能力 跨患者特定变量的纤维化。我们已经组建了一支拥有专业知识的调查团队， 计算建模、体外生物反应器、高级显微镜、成纤维细胞和基质生物学、临床 评估和治疗心力衰竭，并进行生物统计分析，以实现以下目标： 目标1A将检验模型预测的假设，即机械加载可以敏化、减敏和反转 成纤维细胞对生化信号的反应；Aim 1B将检验机械负荷可以 以异构体特异性的方式增加和减少基质金属蛋白酶介导的胶原降解；目标2将整合 细胞内和细胞外网络模型和检验模型预测的基质周转动力学 文献中提供的心脏纤维化时间进程；Aim 3将测试基于模型的患者预后。 特定的化学-机械-环境。这项工作的成功完成将(1)揭示基本的生物学 了解化学-机械相互作用调节胶原重塑，以及(2)产生一种公开的 现有的计算模型能够预测个性化的化学机制下的心脏纤维化。 背景。我们的后续工作将利用这一模型进行计算机药物筛选，以改进目前的治疗方法 根据患者的具体情况进行选择，并发现控制组织纤维化的新治疗靶点。

PROJECT SUMMARY Cardiac fibrosis is a major contributor to diastolic and systolic dysfunction for millions of heart failure patients. Unfortunately, current prediction and control over cardiac fibrosis are lacking due in part to complexity within collagen regulation networks, and in part to patient-to-patient variabilities in the biochemical and mechanical cues that regulate collagen turnover. Our overarching hypothesis is that computationally integrating multiple biochemical and mechanical signaling pathways (rather than a single biomarker) will enable personalized fibrosis risk predictions and improved therapy selection. In preliminary work, we have developed two unique, large-scale network models spanning critical collagen regulation processes: a cardiac fibroblast intracellular signaling network and an extracellular collagen-MMP-TIMP interaction network. For the proposed work, we will integrate the intracellular and extracellular network models with new cell culture experiments, existing animal experiments, and existing patient datasets in order to test the model’s ability for predicting cardiac fibrosis across patient-specific variabilities. We have assembled a team of investigators with expertise spanning computational modeling, in vitro bioreactors, advanced microscopy, fibroblast and matrix biology, clinical assessment and treatment of heart failure, and biostatistical analysis, in order to accomplish the following aims: Aim 1A will test the model-predicted hypothesis that mechanical loading can sensitize, desensitize, and reverse fibroblast signaling responses to biochemical cues; Aim 1B will test the hypothesis that mechanical loading can increase and decrease MMP-mediated collagen degradation in an isoform-specific manner; Aim 2 will integrate the intracellular and extracellular network models and test model-predicted matrix turnover dynamics against cardiac fibrosis time-courses available in the literature; and Aim 3 will test model-based prognosis across patient- specific chemo-mechano-contexts. Successful completion of this work will (1) uncover fundamental biological understanding of chemo-mechano-interactions regulating collagen remodeling, and (2) produce a publicly available computational model capable of predicting cardiac fibrosis given a personalized chemo-mechano- context. Our follow-up work will utilize this model for computational drug screens to improve current therapy selection for patient-specific conditions and to discover novel therapeutic targets for controlling tissue fibrosis.