Collaborative Research: Genome editing approaches to unravel microRNA roles in stochastic multistable networks

生物学的一个基本问题是理解驱动细胞决策的基因调控网络的作用；细胞的决定驱动着一切，从有机体的发育到细胞健康或患病的命运。MicroRNAs （miRNAs）是结合靶基因mRNA的小RNA分子，是基因表达的调节因子。先前的研究已经证明了mirna在细胞生长和细胞分化等多种生物过程中的关键作用。然而，目前尚不清楚的是，多个miRNA分子靶向同一mRNA的不同结合位点可能产生的协同效应，以及miRNA相互作用如何在复杂的基因调控网络中发挥作用。为了解决这些问题，本项目将开发一个结合基因组编辑、活细胞成像和数学建模的跨学科平台。来自德克萨斯大学达拉斯分校的项目的更广泛影响将包括支持国际基因工程机器（iGEM）团队，为当地学校（Plano ISD）和夏令营开发定制教育模块，组织生物和物理科学界面的公共教育活动，以及招募未被充分代表的少数民族。在东北大学方面，该小组将利用研究人员参与美国国家科学基金会理论生物物理中心正在进行的多样性努力，从代表性不足的本科生中招募本科生参与该项目，并带头努力为生物工程专业的本科生创建建模和计算跟踪。最后，这两个小组将直接参与到与当地生物医学小组的接触中，以创造更多的赞赏，通过将先进的工具（如CRISPR）与最先进的计算方法（包括机制研究和机器学习方法）相结合，可以取得快速进展。在决定上皮-间充质转化（EMT）和间充质-上皮转化（MET）表型的复杂关系的核心是一个由转录因子和microrna组成的核心调控单元。该项目将重点研究多个细胞系中EMT细胞决策过程中，针对EMT主转录因子（TF）家族、SNAIL和ZEB的mirna。该团队将首先对转录因子家族SNAIL和ZEB的3'-UTR上的miRNA结合位点进行基于crispr的筛选和定制基因组和碱基编辑修饰。将评估结合位点修饰在EMT和分离的各自克隆中的效果。其次，团队将在活细胞中制备并优化RNA成像平台，测量相应基因的miRNA、mRNA和蛋白水平的时间序列数据和种群分布。利用这些数据，该团队将开发miRNA调控的随机动力学模型，并推断多个miRNA物种结合到同一mRNA的多个位点的组合效应。第三，该团队将把每种miRNA相互作用的动力学模型整合到不同细胞系的全转录因子-miRNA网络模型中。通过对不同EMT状态下基因表达分布和细胞分布的实验观察来校准模型预测，将改进模型。该项目汇集了在基因组编辑/系统生物学（Bleris），上皮-间质网络（Levine）和系统生物学/数学（Lu）方面拥有丰富经验的研究人员。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，认为值得支持。

One of the fundamental questions in biology is to understand the roles of the gene regulatory networks driving cellular decisions; cellular decisions drive everything from an organism's development to a cell's fate as healthy or diseased. MicroRNAs (miRNAs) are small RNA molecules that bind to the mRNA of target genes, acting as regulators of gene expression. Previous studies have demonstrated the critical roles of miRNAs in a variety of biological processes such as cell growth and cell differentiation. However, what is still not well understood concerns possible synergistic effects from multiple miRNA molecules targeting different binding sites of the same mRNA and concerns how miRNA interactions operate within a complex gene regulatory network. To address these issues, an interdisciplinary platform that combines genome editing, live-cell imaging, and mathematical modeling will be developed in this project. The broader impacts of the project from the University of Texas at Dallas side will include support for the International Genetically Engineered Machine (iGEM) team and developing custom educational modules for local schools (Plano ISD) and summer camps, organizing public educational events at the interface of the biological and physical sciences, and the recruitment of underrepresented minorities. From the Northeastern University side, the group will take advantage of the investigators' participation in the NSF Center for Theoretical Biological Physics ongoing diversity efforts to recruit undergraduates from under-represented to work on this project, and spearhead an effort to create a modeling and computational track for undergraduate Bioengineering majors. Finally, both groups will be directly involved in reaching out to local biomedical groups to create more appreciation for the types of rapid progress that can be made by combining advanced tools such as CRISPR with state-of-the-art computational methodology including both mechanistic studies and machine learning approaches. Lying at the heart of intricate relationships that determine the epithelial-mesenchymal transition (EMT) and mesenchymal-epithelial transition (MET) phenotypes is a core regulatory unit that consists of transcription factors and microRNAs. The project will focus on miRNAs targeting the master transcription factor (TF) families of EMTs, SNAIL and ZEB during the cellular decision process of EMT in multiple cell lines. The team will first perform CRISPR-based screens and custom genome and base editing modifications on miRNA binding sites that are located at the 3'-UTR of the transcription factor families SNAIL and ZEB. The effects of binding site modifications in EMT and isolated respective clones will be evaluated. Second, the team will prepare and optimize an RNA imaging platform in live cells and measure time-series data and population distributions for miRNA, mRNA and protein levels of corresponding genes. Using this data, the team will develop stochastic kinetic models of miRNA regulation and infer the combinatorial effects of multiple miRNA species binding to multiple sites of the same mRNA. Third, the team will integrate the kinetic models for each miRNA interaction into full transcription factor-miRNA network models for different cell lines. The models will be refined by calibrating model predictions with experimental observations on the distributions of gene expression and the distribution of cells in various EMT states. This project brings together investigators who have extensive experience in genome editing/systems biology (Bleris), epithelial–mesenchymal networks (Levine), and systems biology/mathematics (Lu). This award reflects NSFs statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.