Binding Kinetics in Transcription Activation and Repression

转录激活和抑制中的结合动力学

• 批准号: 10638937
• 负责人: Timothee Lionnet
• 金额: $ 63.06万
• 依托单位: NEW YORK UNIVERSITY SCHOOL OF MEDICINE
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-06 至 2027-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10638937
• 关键词: Address; Adopted; Affinity; Architecture; Binding; Biological Assay; Biotechnology; Cells; Chromatin; Code; Complex; DNA; DNA Binding; DNA Binding Domain; DNA Sequence; DNA-Protein Interaction; Data; Disease; Engineering; Environment; Event; Exhibits; Genes; Genetic Determinism; Genetic Transcription; Genome; Goals; Health; Human; Kinetics; Link; Maps; Measures; Microscopy; Modeling; Mutation; Nuclear; Output; Property; Proteins; Repression; Sampling; Scanning; Site; Specific qualifier value; Specificity; Synthetic Genes; Time; Transcriptional Activation; Transcriptional Regulation; Variant; Vertebral column; Visualization; Zinc Fingers; base; design; experimental study; gene repression; genetic variant; imaging approach; imaging platform; in vitro Assay; inorganic phosphate; interest; large scale data; model design; novel; programs; protein protein interaction; reconstitution; recruit; residence; single molecule; sound; trait; transcription factor; trend

Transcription regulation is a key determinant of how genetic variability is interpreted by a cell: more than 90% of traits- and disease-associated genetic variants map outside of the coding genome. While we now have a robust understanding of where human TFs might bind, enabling predictions of TF binding when data is unavailable, those approaches do not tell us how frequently a TF binds, how long it stays bound, and what it does once bound, which makes it difficult to predict the impact that TF or regulatory site variants will have on transcription. Critical regulatory architectures, primarily studied with activators, typically favor transient, lower affinity interactions between regulators and the DNA over stronger ones. This is consistent with observations that the transcription machinery can load very efficiently, within seconds. Whether similar strategies are adopted by repressors is unclear, given the much longer timescales over which repression unfolds. In this proposal, we will build upon a novel AI-based Zinc Finger design model to engineer synthetic mimics of endogenous DNA binding domains that bind arbitrary sequences with tunable affinity and measure their binding kinetics in reconstituted assays. We will deploy cutting-edge single-molecule tracking microscopy in order to measure the binding kinetics of these domains to their targets and at non-specific site inside living cells. These experiments will enable reconstructing the strategies deployed by Transcription Factors to find their targets in the genome haystack. We will fuse these DNA binding domains with activating or repressing domains in order to directly link Transcription Factor binding kinetics to the timing and output of transcription bursts synthesized by their target genes. Together, these data will provide mechanistic access to the strategies that repressors and activators have evolved to find and regulate their targets. The results constitute key design principles for novel biotechnologies based on Transcription Factor reprogramming.

转录调控是细胞如何解释遗传变异的关键决定因素：90%以上 与性状和疾病相关的遗传变异的图谱位于编码基因组之外。虽然我们现在有一个 对人类TF可能在哪里结合的可靠理解，从而能够预测当数据被 这些方法不可用，它们不能告诉我们TF绑定的频率、绑定的时间以及绑定的内容 一旦绑定，这就很难预测TF或受监管的站点变体将对 抄写。 主要用激活剂研究的关键监管架构通常倾向于瞬时的、低亲和力的 监管者和DNA之间的相互作用超过了更强的监管者。这与观察结果是一致的，即 转录机器可以非常高效地加载，只需几秒钟。是否采取了类似的策略 考虑到镇压展开的时间尺度要长得多，抑制者尚不清楚。 在这个方案中，我们将建立一个新的基于人工智能的锌指设计模型来设计合成模仿 内源性DNA结合域，以可调的亲和力结合任意序列并测量其 重组分析中的结合动力学。我们将在中国部署尖端的单分子跟踪显微镜 为了测量这些结构域与它们的靶标和体内非特异性位点的结合动力学 细胞。这些实验将使重建转录因子部署的策略能够找到 他们在基因组干草堆中的目标。 我们将这些DNA结合结构域与激活或抑制结构域融合，以便直接连接 转录因子结合动力学对其靶标合成的转录突发的时间和输出的影响 基因。总而言之，这些数据将提供对抑制者和激活者的策略的机械访问 已经进化到寻找和管理它们的目标。这些结果构成了小说的关键设计原则 基于转录因子重新编程的生物技术。