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

生物学中的一个基本问题是理解基因调控网络驱动细胞决策的作用;细胞决策驱动从生物体发育到细胞健康或患病的命运的一切。microRNA（miRNAs）是与靶基因的mRNA结合的小RNA分子，作为基因表达的调节因子。先前的研究已经证明了miRNAs在多种生物学过程中的关键作用，例如细胞生长和细胞分化。然而，对于多个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，仍然没有很好的理解。为了解决这些问题，该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。德克萨斯大学达拉斯分校的该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，在生物和物理科学的界面组织公共教育活动，以及招募代表性不足的少数民族。从东北大学方面来看，该小组将利用研究人员参与NSF理论生物物理中心正在进行的多样性努力，从代表性不足的大学生中招募本科生参与该项目，并率先努力为本科生物工程专业创建建模和计算轨道。最后，两个小组将直接参与与当地生物医学团体的接触，以更好地了解通过将CRISPR等先进工具与最先进的计算方法（包括机械研究和机器学习方法）相结合所能取得的快速进展。 位于决定上皮-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由转录因子和microRNA组成的核心调节单元。该项目将重点关注在多个细胞系中EMT的细胞决策过程中靶向EMT，SNAIL和ZEB的主转录因子（TF）家族的miRNA。该团队将首先对位于转录因子家族SNAIL和ZEB的3 '-UTR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。将评价EMT和分离的相应克隆中结合位点修饰的影响。其次，该团队将准备和优化活细胞中的RNA成像平台，并测量相应基因的miRNA，mRNA和蛋白质水平的时间序列数据和群体分布。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种类与同一mRNA的多个位点结合的组合效应。第三，研究小组将把每个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。将通过对基因表达分布和各种EMT状态下细胞分布的实验观察校准模型预测来完善模型。该项目汇集了在基因组编辑/系统生物学（Bleris），上皮间充质网络（Levine）和系统生物学/数学（Lu）方面具有丰富经验的研究人员。 该奖项反映了NSF的法定使命，并被认为是值得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的支持。

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.