Systematic Assessment of Combinatorial Transcription Factor Activity

组合转录因子活性的系统评估

• 批准号: 10897439
• 负责人: Gary Chung Hon
• 金额: $ 40万
• 依托单位: UT SOUTHWESTERN MEDICAL CENTER
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10897439
• 关键词: Acceleration; Address; Algorithms; Behavior; Benchmarking; Biological Assay; Biomedical Research; Cardiovascular system; Catalogs; Cells; Characteristics; Communities; Community Networks; Complex; Computer Analysis; Consensus; Data; Databases; Embryo; Engineered Gene; Engineering; Fibroblasts; Gene Expression; Genes; Genetic Transcription; Genome; Germ Cells; Germ Layers; Goals; Health; Heart; Human; Knowledge; Libraries; Linear Regressions; Maps; Mathematics; Measurement; Measures; Mission; Modeling; Molecular; Mus; Performance; Plants; Public Health; Publishing; Regenerative Medicine; Research; Resources; Structure; Testing; Time; Transcription Factor 3; United States National Institutes of Health; Work; cell age; cell type; cellular engineering; combinatorial; computer framework; field study; flexibility; gene regulatory network; improved; in vivo; innovation; insight; knowledgebase; novel; overexpression; programs; single-cell RNA sequencing; tool; transcription factor; transcription regulatory network; transcriptional reprogramming; transcriptome; transcriptomics

PROJECT SUMMARY Despite vast catalogs of transcriptomes, our understanding of transcriptional state remains mostly descriptive. In this proposal, we seek to advance the field towards a predictive understanding of cell state by addressing the fundamental gap in knowledge: what are the genes and regulatory networks that drive a cell’s transcriptional state? This knowledge will be critical to understand the molecular basis of cell state and to manipulate cell state for biomedical applications. Transcription factors (TFs) cooperatively drive gene regulatory networks (GRNs) to establish transcriptional states. Notably, forced induction of TFs can reprogram gene expression states by sup- planting existing GRNs. Thus, TFs and GRNs are the building blocks to a predictive understanding of a cell’s transcriptional state. One key challenge is that, in general, the relationship between TFs and GRNs is not known and is difficult to accurately predict. This challenge arises from several current problems: a lack of ground truth GRNs derived from experimental TF perturbation, a reliance on static transcriptomic databases to infer GRNs for TF cocktail prediction, and the difficulty of predicting non-linear TF behaviors. Until we can understand how TFs cooperatively influence GRNs, our ability to predict the TF drivers of cell state will remain limited. Our long- term goal is to understand the molecular basis of transcriptional state for applications in cellular engineering. Towards this goal, the objective of this proposal is to generate a unique resource to directly measure GRNs for simple combinations of TFs, and to use this functional knowledgebase to predict and benchmark complex TF cocktails for transcriptional reprogramming. We hypothesize that experimentally-derived GRNs will improve the performance of predicted TF cocktails for transcriptional reprogramming. Our rationale is that these studies will 1) provide a novel and urgently needed resource of TF functional activity and experimentally-derived GRNs for the community, 2) provide insights into the molecular drivers of transcriptional state, and 3) identify TFs with potential to engineer gene expression states for biomedical research. We propose the following specific aims: (Aim 1) Measure the combinatorial activities of transcription factors; (Aim 2) Improve computational frameworks to predict TF cocktails for transcriptional reprogramming; (Aim 3) Generalize TF-driven GRNs across initial cell contexts and benchmark predictions. This proposal is innovative because it will use our single-cell platform Re- program-Seq 2.0 for high-throughput transcriptional reprogramming to generate a unique resource of functional activities for TFs and GRNs. We expect this resource to propel new research horizons. This proposal is signifi- cant because it will expand our understanding of genome function by quantifying combinatorial TF activity, ex- perimentally deriving GRNs, and providing new tools to engineer gene expression states.

项目摘要 尽管转录组大量目录，但我们对转录状态的理解仍主要是描述性的。 在该提案中，我们试图通过解决该领域的范围，通过解决细胞状态的预测理解 知识的根本差距：驱动细胞转录的基因和调节网络是什么 状态？这些知识对于了解细胞态的分子基础并操纵细胞状态至关重要 用于生物医学应用。转录因子（TFS）合作将基因调节网络（GRN）驱动到 建立转录状态。值得注意的是，TF的强迫诱导可以通过sup-重新编程基因表达态 种植现有的grns。这是对细胞的预测理解的基础，TFS和GRN是 转录状态。一个关键的挑战是，总的来说，TFS与GRN之间的关系尚不清楚 并且很难准确预测。这个挑战是由于当前的几个问题引起的：缺乏地面真相 源自实验TF扰动的GRN，这是对静态转录组数据库的缓解，以推断GRNS 对于TF鸡尾酒预测，以及预测非线性TF行为的困难。直到我们了解如何 TFS合作影响GRN，我们预测细胞状态的TF驱动因素的能力将保持限制。我们的长期 术语目标是了解用于细胞工程中应用的转录状态的分子基础。 为了实现这一目标，该提案的目的是生成独特的资源，以直接测量GRNS TF的简单组合，并使用此功能知识基础来预测和基准测试复杂的TF 转录重编程的鸡尾酒。我们假设实验衍生的GRN将改善 用于转录重编程的预测TF鸡尾酒的性能。我们的理由是这些研究将 1）提供了一种新颖且急需的TF功能活动资源和实验衍生的GRNS 社区，2）提供有关转录状态分子驱动因素的见解，3）用 潜在设计基因表达的生物医学研究状态。我们提出以下具体目标： （目标1）测量转录因子的组合活性； （目标2）改善计算框架 预测TF鸡尾酒用于转录重编程； （AIM 3）在初始细胞中概括TF驱动的GRN 上下文和基准预测。该建议具有创新性，因为它将使用我们的单细胞平台重新使用 program-seq 2.0用于高通量转录重新编程，以生成功能的独特资源 TFS和GRNS的活动。我们希望这种资源能够推动新的研究视野。该提议显着 之所以不能，是因为它会通过量化组合TF活性来扩展我们对基因组功能的理解 仔细地得出GRN，并为工程基因表达态提供新工具。