Coupling between cell cycle arrest and epithelial-to-mesenchymal transition in renal fibrosis development

摘要 细胞是由大量相互作用的分子物种组成的复杂系统 形成监管网络。一个基本的问题是，调控网络如何控制细胞动力学， 尤其是细胞表型。具体地说，细胞周期是一个基本的细胞过程，并与其他过程相耦合。 最近的研究表明，急性肾损伤后细胞周期调节和上皮向间充质转化 肾上皮细胞的转化(EMT)是肾脏修复和肾纤维化进展的中心环节。 因此，调节细胞周期和EMT之间的偶联成为一种潜在的新药 目标。拟议的研究是为了系统地获得关于偶联的全基因组、无偏见的信息。 EMT与细胞周期调控网络之间的耦合机制。此外，我们还将 识别状态空间中的转换路径，即在单元状态期间发生的事件序列 过渡。了解这些信息可以减少搜索药物靶点所需的实验努力 调整这些转变。为了达到这些目的，我们将利用单细胞的一些最新发展 技术，它可以提供大量的数据，这些数据可能被用作 建立数学模型。在目标1中，我们将跟踪转化生长因子-β处理的人肾HK2的单细胞轨迹。 在复合多维细胞特征空间中以增殖细胞核抗原为细胞周期报告基因的细胞和A549细胞 使用组合的无标签和荧光成像以及基于机器学习的图像分析和测试 分析单细胞rna-seq数据预测，EMT可能通过G1/S或G2/M期停滞进行。在……里面 此外，我们将把我们的scRNA-seq分析管道应用于现有的单细胞肾脏数据集，以检查 在活体条件下已识别的转移路径的相关性。在目标2中，我们将破译EMT/细胞周期 在动态系统理论中通过分析scRNA-seq数据和其他类型数据来耦合网络 用于调节过渡过程。从精心策划的细胞周期和EMT的数学模型开始 我们将构建EMT-G1/S耦合和EMT-G2/M耦合的复合数学模型 通过结合scRNA-seq数据分析，开发了我们开发的发电机的确认功率 定量基因调控信息预测方法与传统文献模型 施工方法。我们将测试扰动和基因表达谱的预测效果 通过联合活细胞成像的过渡路径，随后进行多重空间基因组学研究，以及 连接细胞特征和表达特征的机器学习。拟议的研究将提供 肾上皮细胞细胞周期停滞与EMT偶联的机制研究 研究不同细胞程序之间耦合的一般框架。该项目的结果将是 关于缩小细胞周期相关药物靶点以阻断或改变EMT途径以减弱或 甚至使用培养细胞和体内模型逆转肾脏纤维化。

SUMMARY A cell is a complex system composed of a large number of molecular species that interact with each other to form a regulatory network. A fundamental question is how a regulatory network controls cellular dynamics, especially cell phenotypes. Specifically, cell cycle is a basic cellular process and couples to other processes. Recent studies indicate that after acute kidney injury cell cycle regulation and epithelial-to-mesenchymal transition (EMT) of kidney epithelial cells are central to kidney repair and kidney fibrosis progression. Therefore, regulating coupling between cell cycle and EMT emerges as a potentially new pharmaceutical target. The proposed research is to systematically obtain genome-wide, unbiased information on the coupling between the coupling mechanism between EMT and cell cycle regulatory networks. Furthermore, we will identify the transition paths in the state space, i.e., the sequence of events taking place, during the cell state transition. Knowing the information can reduce the needed experimental efforts of searching the drug targets to modulate the transitions. For these purposes we will exploit some recent developments of single cell technique, which can provide large amounts of data that can potentially be used as experimental input for building mathematical models. In Aim 1, we will track single cell trajectories of TGF-β-treated human renal HK2 cells and A549 cells with PCNA as a cell cycle reporter in a composite multi-dimensional cell feature space using combined label-free and fluorescent imaging and machine-learning-based image analyses, and test predictions from analyzing single cell RNA-seq data that EMT proceeds through either G1/S or G2/M arrest. In addition, we will apply our scRNA-seq analysis pipeline to existing single cell renal datasets to examine the relevance of identified transition paths under in vivo conditions. In Aim 2, we will decipher the EMT/cell cycle coupling network through analyzing scRNA-seq data and other types of data within dynamical systems theory for modulating the transition process. Starting with well-curated mathematical models of cell cycle and EMT regulations, we will construct composite mathematical models of EMT-G1/S coupling and EMT-G2/M coupling through combining scRNA-seq data analyses exploiting the confirmed power of our developed dynamo approach on predicting quantitative gene regulation information and conventional literature-based model construction methods. We will test predicted effects of perturbations and gene expression profiles along transition paths through combined live-cell imaging followed by multiplex spatial genomics studies, and machine-learning that connects cell features and expression profiles. The proposed research will provide mechanistic understanding of coupling between cell cycle arrest and EMT in kidney epithelial cells, and a general framework for studying coupling between different cellular programs. The outcome of the project will guide on narrowing down cell-cycle-related drug targets for blocking or changing the EMT paths to attenuate or even revert kidney fibrosis using both cultured cells and in vivo models.