Understanding valvular fibroblast mechanical memory using photo-tunable PEG hydrogels

项目摘要 主动脉瓣狭窄（AVS）是一种进行性疾病，其特征是细胞外基质过度沉积， 基质（ECM）成分在主动脉瓣，导致增加的瓣膜硬度和最终的心力衰竭。 不幸的是，目前唯一的治疗方法是侵入性外科瓣膜置换或修复。一种非手术 治疗AVS的替代方案将减少与手术相关的并发症，然而， 药物治疗受到对疾病进展不完全理解的限制。临床 一致认为，AVS的早期阶段的特征在于常驻成纤维细胞（VIC）的持续活化。 在健康组织中，VIC瞬时激活成肌成纤维细胞以修复受损组织。在疾病中，慢性暴露 组织硬度增加阻止肌成纤维细胞逆转为静止VIC，导致持续性 激活肌成纤维细胞。这种时间依赖性的肌成纤维细胞持久性意味着VIC具有机械的 他们对过去环境的记忆。间充质干细胞还具有机械记忆， 通过染色质重塑来维持。在本提案中，我们试图了解监管机制 负责肌成纤维细胞的持久性，这将提供对AVS进展的见解，并确定潜在的 治疗目标我们假设染色质重塑在肌成纤维细胞的持久性中起作用。在Aim I中， 我们将确定表观遗传学在肌成纤维细胞持久性中的作用。首先，我们将识别机械提示 导致短暂或持续激活的肌成纤维细胞。我们将使用光可调PEG水凝胶， 水凝胶模量可以通过UV光暴露来调节，以获得模拟健康人的硬度的模量， 和纤维化组织。我们将首先培养VIC不同的时间在硬水凝胶，其次是在原位模量 降低至较软的水凝胶硬度以模拟天然组织。在指定时间点恢复后，VIC将 使用已建立的肌成纤维细胞标记物分析持久性。为了确定机械提示是否起作用 在染色质重塑中，我们将识别瞬时和持续染色质结构之间的差异 通过1）甲基化和乙酰化的免疫荧光，2）RT-qPCR测量基因 常用染色质修饰因子的表达; 3）用MATLAB算法测量染色质凝聚。最后， 我们将使用染色质重塑抑制剂来确定表观遗传学是否在持久性中起作用。我们将培养 在有和没有抑制剂的情况下，在诱导肌成纤维细胞持久性的条件下观察VIC，以确定 通过建立的肌成纤维细胞标记物测量持久性的改变。在目标II中，我们将描述 通过检查RNA，瞬时和持续激活的肌成纤维细胞之间的分子差异 每个肌成纤维细胞群体的转录组和鉴定差异表达的基因和信号传导 两者之间的道路。我们将通过阻断从 用siRNA进行转录组分析。

PROJECT SUMMARY Aortic valve stenosis (AVS) is a progressive disease characterized by excessive deposition of the extracellular matrix (ECM) components in the aortic valve, leading to increased valve stiffness and eventual heart failure. Unfortunately, the only current treatment is invasive surgical valve replacement or repair. A non-surgical alternative for treating AVS would reduce complications related to surgery, however, developing a pharmacological treatment has been limited by an incomplete understanding of disease progression. The clinical consensus is that early stages of AVS are characterized by persistent activation of resident fibroblasts (VICs). In healthy tissue, VICs transiently activate to myofibroblasts to repair injured tissue. In disease, chronic exposure to increased tissue stiffness prevents reversal of myofibroblast to quiescent VICs, resulting in persistently activated myofibroblasts. This time-dependent myofibroblast persistence implies VICs possess a mechanical memory of their past environments. Mesenchymal stem cells also possess a mechanical memory which is maintained through chromatin remodeling. In this proposal, we seek to understand the regulatory mechanisms responsible for myofibroblast persistence that will provide insights into AVS progression and identify potential therapy targets. We hypothesize that chromatin remodeling plays a role in myofibroblast persistence. In Aim I, we will determine the role of epigenetics in myofibroblast persistence. First, we will identify the mechanical cues that lead to transiently or persistently activated myofibroblasts. We will use photo-tunable PEG hydrogels where the hydrogel modulus may be tuned via UV light exposure to achieve moduli that mimic the stiffness of healthy and fibrotic tissues. We will initially culture VICs for varying times on stiff hydrogels, followed by an in situ modulus reduction to a softer hydrogel stiffness to mimic native tissue. After recovery at specified time points, VICs will be analyzed for persistence using established myofibroblast markers. To identify if mechanical cues play a role in chromatin remodeling, we will identify chromatin architecture differences between transient and persistent myofibroblasts by 1) immunofluorescence of methylation and acetylation, 2) RT-qPCR to measure the gene expression of common chromatin modifiers, 3) MATLAB algorithm to measure chromatin condensation. Finally, we will use chromatin remodeling inhibitors to determine if epigenetics plays a role in persistence. We will culture VICs under conditions to induce myofibroblast persistence, with and without the inhibitor, to determine if persistence is altered measured by the established myofibroblast markers. In Aim II, we will characterize molecular differences between the transient and persistently activated myofibroblasts by examining the RNA transcriptome of each myofibroblast population and identifying differentially expressed genes and signaling pathways between the two. We will validate regulatory pathways by blocking candidates identified from the transcriptome analysis with siRNAs.