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Systems Mechanobiology Modeling for Patient-Specific Cardiac Fibrosis Predictions

用于患者特异性心脏纤维化预测的系统力学生物学建模

基本信息

批准号：
10754034
负责人：
William James Richardson
金额：
$ 31.22万
依托单位：
UNIVERSITY OF ARKANSAS AT FAYETTEVILLE
依托单位国家：
美国
项目类别：
财政年份：
2019
资助国家：
美国
起止时间：
2019-01-07 至 2023-12-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10754034
关键词：
American Animal Experiments Automobile Driving Biochemical Biological Biological Assay Biological Markers Biology Biometry Bioreactors Cardiac Cell Culture Techniques Cell secretion Chemicals Cicatrix Clinical Clinical Treatment Clinical assessments Collagen Complex Computer Models Coupled Cues Data Set Development Drug Screening Enzyme-Linked Immunosorbent Assay Equation Extracellular Matrix Feedback Fibroblasts Fibrosis Functional disorder Gel Heart Heart failure IL6 gene In Vitro Infarction Inhibition of Matrix Metalloproteinases Pathway Interleukin-1 Interstitial Collagenase Kinetics Literature Matrix Metalloproteinases Measurement Measures Mechanics Mediating Metalloproteases Microscopy Modeling Monitor Myocardial Myocardial Infarction Outcome Pathway interactions Patient Selection Patients Platelet-Derived Growth Factor Prediction of Response to Therapy Process Prognosis Protein Isoforms Proteins Proteomics Publishing Reaction Regulation Reporting Research Personnel Running Rupture Sampling Selection for Treatments Signal Pathway Signal Transduction Stimulus Stretching System TNF gene Testing Therapeutic Time Tissue Inhibitor of Metalloproteinases Tissues Transforming Growth Factor beta Work clinical prognosis coronary fibrosis cytokine desensitization experimental study extracellular follow-up improved kinetic model mechanical load mechanical signal mechanical stimulus network models new therapeutic target patient population patient stratification patient variability pharmacologic predictive modeling prevent prognostic model response risk prediction risk stratification second harmonic generation imaging simulation wound

项目摘要

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.

项目概要心脏纤维化是导致数百万心力衰竭的舒张和收缩功能障碍的主要原因患者。不幸的是，由于复杂性，目前缺乏对心脏纤维化的预测和控制在胶原蛋白调节网络内，部分是由于患者之间的生化和调节胶原蛋白周转的机械信号。我们的首要假设是通过计算整合多种生化和机械信号通路（而不是单一生物标志物）将能够个性化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-MMP-TIMP 相互作用网络。对于提议的工作中，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，以测试模型预测心脏的能力纤维化跨患者特异性变异。我们组建了一支具有专业知识的调查人员团队计算模型、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分析，以实现以下目标：目标 1A 将测试模型预测的假设，即机械负载可以敏化、减敏和逆转成纤维细胞信号对生化信号的反应；目标 1B 将检验机械负载可以以异构体特异性方式增加和减少 MMP 介导的胶原蛋白降解；目标2将整合细胞内和细胞外网络模型以及测试模型预测的矩阵周转动态文献中提供的心脏纤维化时间进程；目标 3 将测试基于模型的患者预后特定的化学机械背景。成功完成这项工作将（1）揭示基本的生物学了解调节胶原蛋白重塑的化学机械相互作用，以及（2）公开发表可用的计算模型能够根据个性化的化学机械预测心脏纤维化语境。我们的后续工作将利用该模型进行计算药物筛选，以改善当前的治疗针对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。

项目成果

期刊论文数量（11）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Effects of Sex and 17 β-Estradiol on Cardiac Fibroblast Morphology and Signaling Activities In Vitro.

性别和 17 β-雌二醇对体外心脏成纤维细胞形态和信号活动的影响。

DOI：
10.3390/cells10102564
发表时间：
2021-09-28
期刊：
Cells
影响因子：
6
作者：
Watts K;Richardson WJ
通讯作者：
Richardson WJ

Fibroblast mechanotransduction network predicts targets for mechano-adaptive infarct therapies.

DOI：
10.7554/elife.62856
发表时间：
2022-02-09
期刊：
eLife
影响因子：
7.7
作者：
Rogers JD;Richardson WJ
通讯作者：
Richardson WJ

Interpretable machine learning predicts cardiac resynchronization therapy responses from personalized biochemical and biomechanical features.

DOI：
10.1186/s12911-022-02015-0
发表时间：
2022-10-31
期刊：
BMC MEDICAL INFORMATICS AND DECISION MAKING
影响因子：
3.5
作者：
Haque, Anamul;Stubbs, Doug;Hubig, Nina C.;Spinale, Francis G.;Richardson, William J.
通讯作者：
Richardson, William J.

Computational screen for sex-specific drug effects in a cardiac fibroblast signaling network model.

DOI：
10.1038/s41598-023-44440-9
发表时间：
2023-10-10
期刊：
SCIENTIFIC REPORTS
影响因子：
4.6
作者：
Watts, Kelsey M.;Nichols, Wesley;Richardson, William J.
通讯作者：
Richardson, William J.

Network modeling predicts personalized gene expression and drug responses in valve myofibroblasts cultured with patient sera.

DOI：
10.1073/pnas.2117323119
发表时间：
2022-02-22
期刊：
Proceedings of the National Academy of Sciences of the United States of America
影响因子：
11.1
作者：
Rogers JD;Aguado BA;Watts KM;Anseth KS;Richardson WJ
通讯作者：
Richardson WJ