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

合作研究：基因组编辑方法揭示随机多稳态网络中 microRNA 的作用

• 批准号: 2114191
• 负责人: Mingyang Lu
• 金额: $ 89.62万
• 依托单位: Northeastern University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-07-15 至 2025-06-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2114191&HistoricalAwards=false
• 关键词: Collaborative; Research; Genome; editing; approaches

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.

生物学的基本问题之一是了解基因网络调节驱动细胞决策的作用；细胞决策驱动从有机体发育到细胞健康或患病的命运的一切。靶基因的 mRNA 作为基因表达的调节因子，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，多种 miRNA 可能产生的协同作用尚不清楚。分子为了解决这些问题，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项目的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，在国际基因工程机器界面组织公共教育活动。生物和物理科学，以及在东北大学方面，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元化努力，招募代表性不足的本科生来从事该项目，并带头努力最后，两个小组将直接参与与当地生物医学团体的接触，以提高人们对将 CRISPR 等先进工具与先进工具相结合所取得的快速进展的认识。最先进的计算方法，包括机制研究和机器学习方法，是决定上皮-间质转化（EMT）和间质-上皮转化（MET）表型的复杂关系的核心，是一个核心调节单元。该项目由转录因子和 microRNA 组成，将重点研究在多个细胞系中 EMT 的细胞决策过程中针对 EMT、SNAIL 和 ZEB 的主转录因子 (TF) 家族的 miRNA。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，EMT 和分离的各自克隆中的结合位点修饰的效果将。其次，该团队将在活细胞中准备和优化 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，该团队将开发随机动力学模型。 miRNA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完整转录因子-miRNA 网络模型中。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来校准模型预测，该项目汇集了在基因组编辑/系统生物学 (Bleris)、上皮-间质网络方面拥有丰富经验的研究人员。 （Levine）和系统生物学/数学（Lu）。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。