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Binding Kinetics in Transcription Activation and Repression

转录激活和抑制中的结合动力学

基本信息

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
项目类别：
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

项目摘要

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：超过90%