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

总结 细胞是由大量分子种类组成的复杂系统，这些分子种类彼此相互作用， 形成监管网络。一个基本的问题是调节网络如何控制细胞动力学， 尤其是细胞表型。细胞周期是细胞的一个基本过程，与其他过程相互耦合。 最近的研究表明，急性肾损伤后，细胞周期调控和上皮细胞间质细胞的分化， 肾上皮细胞的EMT是肾修复和肾纤维化进展的核心。 因此，调控细胞周期与EMT之间的偶联成为一种潜在的新药物 目标拟议的研究是系统地获得全基因组的，无偏见的信息耦合 EMT与细胞周期调控网络之间的耦合机制。此外，我们将 识别状态空间中的转换路径，即，在细胞状态期间发生的事件的顺序 过渡了解这些信息可以减少寻找药物靶点所需的实验工作， 调节过渡。为了这些目的，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，可以潜在地用作实验输入， 建立数学模型。在目标1中，我们将跟踪TGF-β处理的人肾HK 2的单细胞轨迹， 细胞和A549细胞与PCNA作为细胞周期报告基因在复合多维细胞特征空间中的表达 使用组合的无标记和荧光成像以及基于机器学习的图像分析， 通过分析单细胞RNA-seq数据预测，EMT通过G1/S或G2/M停滞进行。在 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， 在体内条件下识别的转变路径的相关性。在目标2中，我们将破译EMT/细胞周期 通过分析scRNA-seq数据和动力系统理论中的其他类型的数据， 来调节过渡过程。从精心策划的细胞周期和EMT数学模型开始 在此基础上，建立了EMT-G1/S耦合和EMT-G2/M耦合复合数学模型 通过结合scRNA-seq数据分析， 一种预测定量基因调控信息的方法和传统的基于文献的模型 施工方法我们将测试预测的扰动和基因表达谱的影响，沿着 通过组合活细胞成像的过渡路径，然后进行多重空间基因组学研究，以及 连接细胞特征和表达谱的机器学习。该研究将提供 对肾上皮细胞中细胞周期阻滞和EMT之间耦合的机制理解，以及 研究不同细胞程序之间耦合的一般框架。该项目的成果将 指导缩小细胞周期相关的药物靶点，以阻断或改变EMT途径， 甚至使用培养的细胞和体内模型来逆转肾纤维化。

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.