Systems Mechanobiology Modeling for Patient-Specific Cardiac Fibrosis Predictions

项目摘要 心脏纤维化是导致数百万心力衰竭患者舒张和收缩功能障碍的主要原因 患者不幸的是，目前缺乏对心脏纤维化的预测和控制，部分原因是复杂性 在胶原蛋白调节网络中，部分与患者之间的生化和 调节胶原蛋白周转的机械线索。我们的首要假设是，计算整合 多种生物化学和机械信号传导途径（而不是单一生物标志物）将使 个性化的纤维化风险预测和改进的治疗选择。在初步工作中，我们开发了 两个独特的，大规模的网络模型，跨越关键的胶原调节过程：心脏成纤维细胞 细胞内信号网络和细胞外胶原-MMP-TIMP相互作用网络。拟议 工作，我们将整合细胞内和细胞外网络模型与新的细胞培养实验， 现有的动物实验和现有的患者数据集，以测试模型预测心脏的能力。 纤维化的患者特异性变异。我们已经召集了一个调查小组， 计算建模，体外生物反应器，先进显微镜，成纤维细胞和基质生物学，临床 心力衰竭的评估和治疗以及生物统计学分析，以实现以下目标： 目的1A将检验模型预测的假设，即机械负荷可以致敏、脱敏和逆转 成纤维细胞对生化信号的信号反应;目的1B将检验机械负荷可以 以同种型特异性方式增加和减少MMP介导胶原降解; Aim 2将整合 细胞内和细胞外网络模型和测试模型预测的基质周转动力学， 心脏纤维化时间进程可在文献中获得;目标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）揭示生物学的基本原理， 了解化学机械相互作用调节胶原蛋白重塑，和（2）产生一个公开的 现有的计算模型能够预测心脏纤维化， 上下文我们的后续工作将利用这一模型进行计算药物筛选，以改善目前的治疗 针对患者特定病症进行选择，并发现控制组织纤维化的新型治疗靶点。

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.