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

二尖瓣畸形的特征是存在两个(而不是三个)叶和 是最常见的先天性心脏畸形，约占总人口的1.4%。据估计，~30%-50% 患有BAV的人将需要对主动脉狭窄进行手术治疗，而且几乎所有患者都存在BAV 50岁以下的主动脉瓣置换术患者。在无症状的年轻患者中，BAV的存在通常是 由于筛查超声心动图的广泛应用和常规使用，早期发现。然而，早些时候 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。药物干预措施的合理设计需要更完整的 了解BAV中导致疾病进展的潜在细胞过程。《居民》 瓣膜间质细胞(VIC)负责维持心脏瓣膜的机械环境， 主要通过合成和重塑细胞外基质(ECM)。更改的ECM内容和组织 在BAV钙化之前已有记录；因此，瓣膜退变是一个潜在的风险 最终在BAV中发展为AS的因素。然而，复杂的机械转导网络 负责VIC激活和随后的病理性ECM合成/重塑使其难以 通过纯通过确定进展为症状性疾病的基础的系统级属性 实验手段。我们假设来自BAVs的受害者对机械刚度的增加更敏感 与三尖瓣对应的主动脉瓣(TAV)相比，BAV的机械结构改变会加剧这些 改变了机械转换级联反应。然而，将改变的机械环境的影响分开 在存在亲密反馈回路的情况下，从固有的VIC差异需要系统级的实验/ 计算方法。因此，我们建议将蛋白质组数据与第一个计算模型相结合 解决这一研究问题的VIC细胞信号。在目标1中，疾病患者的信号网络发生了变化 主动脉瓣置换术中提取的人BAVs和TAVs将通过定量蛋白质组学进行比较 用普通的电视录音设备。在目标2中，将从BAVs和TAVs中提取人VICs，并在机械响应的基础上进行培养 水凝胶，并通过原生生物和共聚焦显微镜进行评估。这些数据将与 用于评价机械转导级联中关键差异的机械转导计算模型 BAV和TAV衍生的VICs之间的关系，并预测扰乱BaV和TAV机械转导的关键组件的影响。 在AIM 3中，将使用BAV和TAV派生的机械波形来刺激VIC 在高通量机械生物学筛选平台上评价机械负荷变化对机体功能的影响 BAV-和TAV-机械转导。这一拟议项目的成功完成将导致 通过改变BAV中的瓣膜机械结构来改变VIC的机械传递，并提供了一个平台 在瓣膜机械结构改变的背景下，识别药物靶点以延缓/预防BAV疾病的进展。

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.