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

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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.