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: 2114192
• 负责人: Leonidas Bleris
• 金额: $ 49.33万
• 依托单位: University of Texas at Dallas
• 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 2026-06-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2114192&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驱动细胞决策的基因调节网络的作用。细胞决策将从组织的发展到牢房的命运，都可以驱动一切。 microRNA（miRNA）是与靶基因mRNA结合的小RNA分子，充当基因表达的调节剂。先前的研究表明，miRNA在各种生物学过程中的关键作用，例如细胞生长和细胞分化。但是，仍然不太了解的是，靶向相同mRNA的不同结合位点的多个miRNA分子可能的协同作用，并涉及miRNA相互作用如何在复杂的基因调节网络中起作用。为了解决这些问题，将在此项目中开发一个结合基因组编辑，实时成像和数学建模的跨学科平台。得克萨斯大学达拉斯方面的项目对该项目的更广泛影响将包括对国际基因工程机器（IGEM）团队的支持，并为当地学校（Plano ISD）和夏令营开发定制的教育模块，在生物学和物理科学的界面上组织公共教育活动，以及未经遗产的未成年人的招募。在东北大学方面，该小组将利用研究人员参与NSF理论生物物理学中心正在进行的多样性努力，以招募从代表性不足的本科生来从事该项目的工作，并努力为培训生物工程专业的少校创建建模和计算轨道。最后，这两个小组都将直接参与与当地的生物医学小组接触，以对可以通过将CRISPR等先进工具与最先进的计算方法相结合，包括机械研究和机器学习方法，对快速进步的类型产生更多的欣赏。位于确定上皮 - 间质转变（EMT）和间质上皮过渡（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。该项目将集中于针对EMT，蜗牛和ZEB的主转录因子（TF）家族的miRNA，在多个细胞系的EMT细胞决策过程中。该团队将首先对位于转录因子家族蜗牛和Zeb的3'-UTR的miRNA绑定站点进行基于CRISPR的屏幕和自定义基因组以及基础编辑修改。将评估EMT和分离的相对克隆中结合位点修饰的影响。其次，该团队将在活细胞中准备和优化一个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人群分布。使用这些数据，团队将开发miRNA调节的随机动力学模型，并推断出多种miRNA物种与同一mRNA的多个位点结合的组合作用。第三，团队将将每种miRNA相互作用的动力学模型集成到不同细胞系的完全转录因子miRNA网络模型中。这些模型将通过对模型预测进行校准，并通过对基因表达的分布和各种EMT状态的细胞分布进行实验观察。该项目汇集了在基因组编辑/系统生物学（BLERIS），上皮 - 米斯质网（Levine）和系统生物学/数学（LU）方面具有丰富经验的研究者。该奖项反映了NSFS法定任务，并被认为是值得通过基金会的智力优点和更广泛影响的审查标准来评估的。