A Computational Model of Traction Force-Induced Fibronectin Fibril Growth

 描述（由申请人提供）：纤维化是细胞外基质的异常组装。它见于几乎所有的器官系统，并导致心脏、肝脏、肺和肾脏疾病中的器官衰竭，同时在恶性肿瘤生长和植入生物材料衰竭中也起着重要作用。尽管其病理学流行，但用于治疗纤维化的有效疗法很少。纤维化是由炎症反应引发的，该炎症反应驱动驻留细胞分化成肌成纤维细胞表型。在正常组织愈合情况下，该过程在原始炎症信号消散后停止。在纤维化中，即使在炎症信号停止后，该过程仍以自我维持的方式继续。了解这些动力学对于理解纤维化至关重要。纤维化起始的关键组分是细胞外基质蛋白纤连蛋白（FN）的细胞来源的组装。纤连蛋白（FN）是一种可溶性蛋白质，其通过细胞组装成弹性的、不溶性的原纤维。尽管对FN原纤维形成的研究超过40年，但该过程仍然没有完全理解。我们所知道的是：细胞拉伸可溶性FN，暴露出一个隐蔽的结合位点，允许第二个FN结合。这个过程继续形成绳状原纤维。组装的FN原纤维含有生长因子结合位点，其将促纤维化生长因子定位在细胞表面。我们假设一个正反馈回路，其中损伤部位的可溶性生长因子启动增加的收缩力和FN表达，从而驱动FN组装。这种组装将促纤维化生长因子聚集在细胞表面， 进一步驱动FN组装、促纤维化生长因子表达和自我维持的纤维化系统。为了研究这一点，我们将开发一个计算生物物理模型，预测FN原纤维的组装反应肌动球蛋白驱动的力量。该模型是建立在先前发表的细胞-基质界面模型基础上的混合随机-确定性模型。该模型将使用新的微加工基板进行验证，该基板允许量化FN原纤维生长和细胞产生的牵引力沿着重组FN蛋白家族，该重组FN蛋白家族允许在拉伸期间观察FN结构域打开。该模型的开发将使我们能够通过预测可测量的宏观事件来研究分子尺度的FN组装过程（包括一种新的假设，其中FN中的每个III型结构域能够结合另一个FN）。然后，我们将模拟促纤维化生长因子TGF-β 1的后续聚类和信号传导，以预测去除初始损伤后导致可持续信号传导的参数空间。将这些模型结果与体外纤维化测定进行比较。我们设想，这个模型将创建一个新的系统生物学模型，其中细胞外基质的生物物理学耦合到可溶性生长因子信号通路。我们相信，这种建模方法可能会极大地改善我们对纤维化的理解，从而影响广泛的病理学。

 DESCRIPTION (provided by applicant): Fibrosis is the aberrant assembly of extracellular matrix. It is seen in nearly all organ systems, and is responsible for organ failure in heart, live, lung, and kidney disease, while also playing a prominent role in malignant tumor growth and implanted biomaterial failure. Despite its pathological prevalence, there are scant effective therapeutics for treating fibrosis. Fibrosis is initiated by an inflammatory response that drives resident cells to differentiate into a myofibroblast phenotype. Under a normal tissue healing scenario, this process ceases upon dissipation of the original inflammatory signal. In fibrosis, this process continues in a self- sustaining manner even after inflammatory signals have ceased. Understanding of these dynamics is crucial to understanding fibrosis. A key component of fibrosis initiation is the cell-derived assembly of the extracellular matrix protein fibronectin Fibronectin (FN) is a soluble protein that is assembled into elastic, insoluble fibrils by cells. Despite over 40 years of research into FN fibril formation, the process is still not completely understood. This much we do know: cells stretch soluble FN, exposing a cryptic binding site that allows for the binding of a second FN. This process continues to form a rope-like fibril. Assembled FN fibrils contain a growth factor binding site that localizes pro-fibrotic growth factor at the cell surface. We hypothesize a positive feedback loop in which soluble growth factors at the site of damage initiate increased contractile forces and expression of FN, which drives FN assembly. This assembly clusters the pro-fibrotic growth factors at the cell surface, which in turn drives further FN assembly, pro-fibrotic growth factor expression, and a self-sustaining fibrosis system. To investigate this, we will develop a computational biophysical model that predicts the assembly of FN fibrils in response to actomyosin-driven forces. This model is a hybrid stochastic-deterministic model that builds on previously published models of the cell-substrate interface. The model will be validated using novel microfabricated substrates that allow for quantification of FN fibril growth and cell-generated traction forces along with a family of recombinant FN proteins that allow for visualization of FN domain opening during stretch. Development of the model will allow us to investigate molecular-scale FN assembly processes (including a novel hypothesis in which each Type III domain in FN is able to bind another FN) by predicting measurable, macro-scale events. We will then model the subsequent clustering and signaling of the pro-fibrotic growth factor TGF-b1 to predict parameter spaces that lead to sustainable signaling following the removal of initial insult. These model outcomes will be compared to in vitro fibrosis assays. We envision that this model will create a new paradigm of systems biology modeling in which the biophysics of the extracellular matrix are coupled to soluble growth factor signaling pathways. We believe that this line of modeling could lead to dramatic improvements in our understanding of fibrosis and thus impact a wide array of pathologies.