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

University Of Pittsburgh At Pittsburgh, Pittsburgh PA

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Abstract

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.