Dynamic Modeling of Mechanotransduction in the Bicuspid Aortic Valve: Separating the Effects of Altered VICs and Mechanics

二叶式主动脉瓣（BAV）异常的特征是存在两个（而不是三个）小叶， 是最常见的先天性心脏畸形，影响约1.4%的人口。据估计，约30-50% 的BAV患者需要手术干预治疗主动脉瓣狭窄，几乎所有患者都存在BAV。 50岁以下的主动脉瓣置换患者。在无症状的年轻患者中，BAV的存在通常是 由于超声心动图筛查的广泛可用性和常规使用，可早期发现。然而，早 诊断只会导致更密切的监测，因为没有药物干预来延迟/预防 BAV中的疾病进展。药物干预的合理设计需要一个更完整的 了解负责BAV疾病进展的潜在细胞过程。驻地 瓣膜间质细胞（VIC）负责维持心脏瓣膜的机械环境， 主要通过细胞外基质（ECM）的合成和重塑。更改ECM内容和组织 在BAV钙化之前已记录;因此，瓣膜退化是一种潜在风险 BAV中AS最终发展的因素。然而，复杂的机械传导网络 负责维克激活和随后的病理性ECM合成/重塑使得难以 通过纯粹的方法来确定系统水平的特性， 实验手段。我们假设BAV的VIC对机械刚度的增加更敏感 与三尖瓣主动脉瓣（TAV）相比，BAV的力学变化加剧了这些情况 改变的机械传导级联。然而，将机械环境改变的影响 从内在的维克差异的存在密切的反馈回路需要一个系统级的实验/ 计算方法因此，我们建议将Proteomcis数据与第一个计算模型相结合 维克细胞信号传导来解决这个研究问题。在目标1中，改变了疾病中的信号网络， 将通过定量蛋白质组学比较在主动脉瓣置换术期间提取的人BAV和TAV 正常的TAV。在目标2中，将从BAV和TAV中提取人VIC，在机械反应性 水凝胶，并通过protoemics和共聚焦显微镜进行评估。这些数据将与 机械传导的计算模型，以评估机械传导级联中的关键差异 之间的BAV和TAV衍生的VIC和预测扰动的Bav和TAV的机械转导的关键组成部分的影响。 在目标3中，BAV和TAV衍生的机械波形将用于刺激VIC 在一个高负荷的机械生物学筛选平台上，评估改变机械负荷对 BAV-和TAV-机械转导。成功完成这一拟议项目将导致 BAV中改变的瓣膜力学导致的改变的维克机械转导，并为计算机模拟提供平台 在瓣膜力学改变的背景下，药物靶点识别可延迟/预防BAV疾病进展。

The bicuspid aortic valve (BAV) anomaly is characterized by the presence of two (rather than three) leaflets and is the most common congenital heart anomaly, affecting~ 1.4% of the population. It is estimated that ~30-50% of individuals with BAV will require surgical intervention for aortic stenosis and a BAV is present in virtually all aortic valve replacement patients under age 50. The presence of a BAV in asymptomatic, young patients is often detected early due to widespread availability and routine use of screening echocardiography. However, early diagnosis only leads to closer monitoring as there are no pharmaceutical interventions for delaying/preventing disease progression in the BAV. The rational design of pharmaceutical interventions warrants a more complete understanding of the underlying cellular processes responsible for disease progression in the BAV. The resident valve interstitial cells (VICs) are responsible for maintaining the mechanical environment of the heart valves, primarily through synthesis and remodeling of the extracellular matrix (ECM). Altered ECM content and organization has been documented prior to calcification in the BAV; thus, valve degeneration is a potential risk factor for the eventual development of AS in the BAV. However, the complex mechanotransduction networks responsible for VIC activation and subsequent pathological ECM synthesis/remodeling has made it difficult to determine the systems-level properties underlying the progression to symptomatic disease through purely experimental means. We hypothesize that VICs from BAVs are more sensitive to increases in mechanical stiffness than their tricuspid aortic valve (TAV) counterparts and the altered mechanics of the BAV exacerbates these altered mechanotransduction cascades. However, separating the effects of the altered mechanical environment from intrinsic VIC differences in the presence of intimate feedback loops requires a systems-level experimental/ computational approach. Thus, we propose to integrate proteomcis data with the first computational model of VIC cell signaling to address this research question. In Aim 1, the altered signaling networks in diseased human BAVs and TAVs extracted during for aortic valve replacement will be compared via quantitative proteomics with normal TAVs. In Aim 2, human VICs will be extracted from BAVs and TAVs, cultured on mechanoresponsive hydrogels, and assessed via protoemics and confocal microscopy. This data will be integrated with computational models of mechanotransduction to evaluate key differences in the mechanotransduction cascade between BAV- and TAV-derived VICs and predict the effects of perturbing key components of Bav- and TAV-mechanotransduction. In Aim 3, BAV- and TAV-derived mechanical waveforms will be used to stimulate VICs on a high throuput mechanobiology screening platform to evaluate the effects of altered mechanical loads on BAV- and TAV-mechanotransduction. Successful completion of this proposed project will result in separation of altered VIC mechanotransduction from altered valve mechanics in the BAV and provide a platform for in silico drug target identification to delay /prevent BAV disease progression in the context of altered valve mechanics.