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

二尖瓣主动脉瓣机械传导的动态建模：分离改变的 VIC 和力学的影响

基本信息

批准号：
10328481
负责人：
Daniel Paul Howsmon
金额：
$ 7.05万
依托单位：
UNIVERSITY OF TEXAS AT AUSTIN
依托单位国家：
美国
项目类别：
财政年份：
2020
资助国家：
美国
起止时间：
2020-09-01 至 2023-08-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10328481
关键词：
Address Affect Age Age-Years Aortic Valve Insufficiency Aortic Valve Stenosis Biological Models Cell physiology Cells Complex Computer Models Confocal Microscopy Coupled Coupling Data Development Diagnosis Disease Disease Progression Dissection Drug Targeting Early Diagnosis Echocardiography Environment Extracellular Matrix Feedback Fibroblasts Fibrosis Future Genes Heart Valves Human Hydrogels Individual Intervention Mechanics Modeling Monitor Natural History Operative Surgical Procedures Pathologic Pathway interactions Patients Pharmacologic Substance Pharmacology Play Population Prevalence Probability Property Proteomics Research Research Personnel Risk Factors Role Signal Pathway Signal Transduction Smooth Muscle Stimulus System Tissues United States National Institutes of Health Work aortic valve aortic valve disorder aortic valve replacement bicuspid aortic valve calcification congenital heart disorder design experience experimental study hemodynamics human disease in silico in vivo intercellular communication interstitial cell mechanical load mechanotransduction mitral valve replacement new therapeutic target patient subsets prevent screening success transcriptome sequencing virtual

项目摘要

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.

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，并且是最常见的先天性心脏异常，影响约 1.4% 的人口。估计~30-50% 的 BAV 患者需要对主动脉瓣狭窄进行手术干预，并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。无症状的年轻患者中经常存在 BAV 由于筛查超声心动图的广泛普及和常规使用，可以及早发现。然而，早诊断只会导致更密切的监测，因为没有药物干预措施可以延迟/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导致 BAV 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，主要通过细胞外基质（ECM）的合成和重塑。改变 ECM 内容和组织在 BAV 钙化之前已被记录；因此，瓣膜退化是一个潜在的风险 BAV 中 AS 最终发展的因素。然而，复杂的力传导网络负责 VIC 激活和随后的病理性 ECM 合成/重塑使其变得困难纯粹通过实验手段。我们假设 BAV 中的 VIC 对机械刚度的增加更敏感与三尖瓣主动脉瓣 (TAV) 对应物相比，BAV 机械结构的改变加剧了这些改变了机械传导级联。然而，分离机械环境改变的影响存在密切反馈环路时，从内在的 VIC 差异需要系统级实验/ 计算方法。因此，我们建议将蛋白质组学数据与第一个计算模型整合 VIC 细胞信号传导来解决这个研究问题。在目标 1 中，患病的信号网络发生了改变主动脉瓣置换术期间提取的人类 BAV 和 TAV 将通过定量蛋白质组学进行比较与普通 TAV 一起。在目标 2 中，将从 BAV 和 TAV 中提取人类 VIC，并在机械响应上培养水凝胶，并通过蛋白质组学和共焦显微镜进行评估。该数据将与用于评估力转导级联中关键差异的力转导计算模型 BAV 和 TAV 衍生的 VIC 之间的差异，并预测扰动 Bav 和 TAV 机械转导关键成分的影响。在目标 3 中，BAV 和 TAV 衍生的机械波形将用于刺激 VIC 在高通量机械生物学筛选平台上评估机械负荷改变的影响 BAV 和 TAV 机械转导。该拟议项目的成功完成将导致通过改变 BAV 中的瓣膜力学来改变 VIC 机械传导，并为计算机模拟提供平台药物靶标识别可在瓣膜力学改变的情况下延迟/预防 BAV 疾病进展。