Reprogramming cell-fate decisions through predictive modeling and synthetic biology

通过预测模型和合成生物学重新编程细胞命运决定

• 批准号: 10784558
• 负责人: JEFF M HASTY
• 金额: $ 3.33万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN DIEGO
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-20 至 2026-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10784558
• 关键词: Affect; Biological Process; Cell Death; Cell Fate Control; Cell Reprogramming; Cells; Chemicals; Complex; Computer Models; Core Protein; Deacetylase; Decision Making; Deterioration; Development; Diabetes Mellitus; Disease; Engineering; Eukaryota; Foundations; Gene Expression; Genes; Genetic Transcription; Growth; Heme; Lysine; Malignant Neoplasms; Mammals; Mediating; Microfluidics; Mitochondria; Mitotic; Modeling; Molecular; Neurodegenerative Disorders; Pattern; Phenotype; Physiological; Play; Process; Proteins; Regulation; Research; Resources; Ribosomal DNA; Role; Saccharomyces cerevisiae; Structure; Synthetic Genes; Time; Yeasts; cell injury; diagnostic strategy; gene conservation; gene interaction; improved; insight; predictive modeling; programs; promoter; protein expression; synthetic biology; tool; treatment strategy

Project Summary Advances in synthetic biology provide powerful tools to interrogate the complex relationship between network structure and function. In this study, we will combine synthetic biology with computational modeling to investigate network-mediated regulation of cell damage and deterioration, a complex biological process. As similar studies in mammals are prohibitively time- and resource-intensive, we choose to focus on Saccharomyces cerevisiae, which has proven to be a genetically tractable model for many fundamental processes in mitotic cells and has allowed identification of many conserved genes that regulate cell-fate decisions in eukaryotes. Emerging questions include how these genes interact and how the interactions change dynamically to drive multi-generational cell deterioration dynamics. We recently found two distinct phenotypes in genetically identical yeast cells as they approach cell death: one with decreased ribosomal DNA (rDNA) silencing and nucleolar decline (Mode 1) whereas the other with heme depletion and mitochondrial decline (Mode 2). We found that stochasticity plays an important role in choosing one of the two paths, but once the fate decision is made, it is almost always irreversible. We identified a core molecular circuit, consisting of the lysine deacetylase Sir2 and the heme-activated protein (HAP) transcriptional complex, that governs the decision to select one of these two paths. Based on the model, we were able to engineer cells to follow a third path with a dramatically extended period of growth and survival, free of deterioration (Mode 3). In this proposal, we will expand these efforts and systematically perturb and rewire the core circuit that controls cell fate in order to reprogram its decision-making process. In Aim 1, we will use chemically-inducible promoters to control expression of Sir2 and HAP and thereby modulate cell-fate decisions in isogenic cells. We will use microfluidics to generate distinct, dynamic patterns of Sir2 and HAP expression and evaluate their effects on damage accumulation, physiological changes, and cellular decline. In Aim 2, we will genetically rewire the core Sir2-HAP circuit under the guidance of computational modeling and examine how these engineered circuits govern cell- fate decisions and cell deterioration dynamics. In Aim 3, we will use high-throughput microfluidics to identify the gene expression programs associated with Mode 1, Mode 2, and Mode 3 and examine how perturbations of these programs affect multi-generational deterioration dynamics. These analyses will uncover the genes and processes that underlie the missing connections between the Sir2-HAP core circuit and downstream modules that underlie cellular decline leading to cell death. They will enable us to expand our computational model and improve its predictive power. Throughout the study, we will construct deterministic and stochastic models, which will produce testable predictions and guide engineering of synthetic gene circuits. If successful, this research will advance a quantitative and predictive understanding of cellular fate decisions and cell deterioration.

项目摘要 合成生物学的进步提供了强大的工具来询问复杂关系 网络结构和功能。在这项研究中，我们将合成生物学与计算模型结合到 研究网络介导的细胞损伤和恶化调节，这是一个复杂的生物学过程。作为 在哺乳动物中类似的研究是过时和资源密集型的，我们选择关注 酿酒酵母，事实证明，它是许多基本的遗传障碍模型 有丝分裂细胞中的过程，并允许鉴定许多调节细胞粘液的保守基因 真核生物的决定。新兴问题包括这些基因的相互作用以及相互作用的变化方式 动态以驱动多代细胞劣化动力学。我们最近发现了两个不同的表型 在接近细胞死亡时遗传上相同的酵母菌细胞中：核糖体DNA降低（rDNA） 沉默和核仁下降（模式1），而另一个则具有血红素耗竭和线粒体下降（模式 2）。我们发现随机性在选择这两条路径之一中起着重要作用，但是一旦命运决定 是制造的，几乎总是不可逆的。我们确定了一个核心分子电路，由赖氨酸组成 脱乙酰基酶SiR2和血红素激活蛋白（HAP）转录复合物，该复合物控制决定 选择这两条路径之一。基于模型，我们能够设计细胞，以沿着第三条路径 生长和生存的延长时期，没有恶化（模式3）。在此提案中，我们将 扩大这些努力并系统地扰动，并重新连接控制细胞命运的核心电路，以便 重新编程其决策过程。在AIM 1中，我们将使用化学诱导的启动子来控制 SiR2和HAP的表达，从而调节等源细胞中的细胞效果决定。我们将使用微流体学 生成SIR2和HAP表达的不同动态模式，并评估其对损害的影响 积累，生理变化和细胞下降。在AIM 2中，我们将在基因上恢复核心Sir2-Hap 在计算建模的指导下电路，并检查这些工程电路如何控制细胞 - 命运决策和细胞恶化动力学。在AIM 3中，我们将使用高通量微流体来识别 与模式1，模式2和模式3相关的基因表达程序，并检查如何扰动 这些程序会影响多代劣化动态。这些分析将发现基因和 Sir2-Hap Core电路和下游模块之间缺少连接的过程是基于缺少连接的过程 细胞下降的基础导致细胞死亡。它们将使我们能够扩展我们的计算模型，并 提高其预测能力。在整个研究中，我们将构建确定性和随机模型，这些模型 将产生可测试的预测并指导合成基因回路的工程。如果成功，这项研究将 提出对细胞命运决策和细胞恶化的定量和预测理解。