Transcriptional Control During Erythropoiesis

红细胞生成过程中的转录控制

• 批准号: 10398185
• 负责人: Marjorie Carole Brand
• 金额: $ 63.29万
• 依托单位: INSTITUTE FOR SYSTEMS BIOLOGY
• 依托单位国家: 美国
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-09-16 至 2025-04-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10398185
• 关键词: Adult; Automobile Driving; Benchmarking; Binding; Blood; Cell Fate Control; Cell Surface Proteins; Cell model; Cell physiology; Cells; Chromatin; Complex; Computer Analysis; Data; Disease; Erythrocytes; Erythroid; Erythroid Cells; Erythropoiesis; FLI1 gene; Gene Expression; Genes; Genetic Transcription; Genomics; Goals; Health; Hematopoietic stem cells; Hemoglobin; Histones; Human; Joints; Knowledge; Mass Spectrum Analysis; Measurement; Measures; Messenger RNA; Methodology; Methods; Mission; Modeling; Nature; Outcome; Patients; Pharmacology; Population; Process; Production; Proteins; Proteome; Proteomics; Proxy; Public Health; Regulation; Research; Role; Specific qualifier value; System; Technology; Testing; Time; Transcript; Transcriptional Regulation; Umbilical Cord Blood; United States National Institutes of Health; Validation; Work; base; beta Globin; beta Thalassemia; comparative; design; dosage; experimental study; fetal; genome-wide; histone modification; human model; improved; in vivo; innovation; insight; network models; novel; novel therapeutic intervention; novel therapeutics; progenitor; programs; temporal measurement; transcription factor; transcription regulatory network; transcriptome

Erythropoiesis is a dynamic process governed by quantitative changes in the relative levels of transcription fac-tors (TFs). Due to the current paucity of quantitative data on the proteins that constitute the transcriptional regulatory network (TRN), most models of erythropoiesis are based primarily on mRNA measurements and do not typically consider changes in the protein levels of specific TFs. This significantly limits the understanding of erythropoiesis and other transcriptionally regulated processes such as ß-globin expression, ultimately impinging on the capacity to correct hemoglobin disorders. The long-term goal is to decipher the TRN that controls erythropoiesis in health and disease. The objective of this proposal is to significantly expand our TRN model for cell fate decision during erythropoiesis by integrating dynamic bulk and single cell TF protein abundance measurements with other transcription-relevant -omics data. The central hypothesis is that relative protein levels of TFs are critical parameters in the establishment of gene expression programs during the continuum of differentiation, and that erythropoiesis is driven by graded changes in the relative amounts of specific combinations of TFs. The rationale is that integration of the dynamic and quantitative nature of the TF proteome into an expanded TRN of erythropoiesis will yield a model with improved predictive power which will serve as a benchmark for healthy erythropoiesis against which to compare erythroid-related disease states, and will facilitate the identification of pharmacological agents to restore normal erythropoiesis. Three specific aims have been designed: 1) Absolute quantification of the TF proteome during erythropoiesis; 2) Determine how changes in the abundance of multiple TFs in single cells initiate and progressively reinforce cell fate decisions along the erythroid trajectory; and 3) Computational analysis, modeling and validation of the erythropoiesis TRN. For the first aim, quantitative mass spectrometry (MS) approaches will be used to measure absolute levels of the TF proteome during ex vivo erythropoiesis of HSPCs derived from healthy donors. For the second aim, complementary CyTOF and targeted-MS proteomic approaches will be used to estimate TF protein abundances in single cells, and other single cell –omics technologies will be used to measure changes in gene expression and TF genomic binding during ex vivo erythropoiesis. For the third aim, TRN models of erythropoiesis will be built utilizing measurements of TF protein abundances, and other transcription-relevant –omics data. Functional validation will be performed for TFs that have been implicated in transcriptional control during erythropoiesis based on our recent results. The approach is innovative because it uses a novel combination of single cell and bulk proteomics methodologies to quantify large numbers of TFs during erythropoiesis in primary human cells and uses the data for integrative TRN modeling. The proposed research is significant because it will illuminate complex regulatory processes that control erythropoiesis. Ultimately, such knowledge has the potential to guide the design of new therapeutics to re-establish proper ß-globin expression in ß-thalassemic patients.

红细胞生成是一个动态过程，该过程受转录FAC-TORS（TFS）相对水平的定量变化所控制的。由于目前缺乏构成转录调节网络（TRN）的蛋白质的定量数据，因此大多数红细胞生成模型主要基于mRNA测量，并且通常不考虑特定TF蛋白质水平的变化。这显着限制了对促红细胞生成的理解和其他转录调节的过程，例如β-珠蛋白的表达，最终影响了纠正血红蛋白疾病的能力。长期目标是决定控制健康和疾病中红细胞生成的TRN。该提案的目的是通过将动态体积和单细胞TF蛋白丰度测量与其他转录相关的 - 元素数据相结合，从而显着扩展了我们在红细胞生成期间细胞命运决策的TRN模型。中心假设是，TF的相对蛋白水平是分化连续过程中基因表达程序建立基因表达程序的关键参数，并且红细胞生成是由TFS特定组合的相对量的分级变化所驱动的。理由是，将TF蛋白的动态和定量性质的整合到扩展的红细胞生成TRN中将产生具有改善预测能力的模型，这将作为健康的红细胞生成的基准，用于与伴有红细胞相关疾病状态的健康状态，并将促进药物剂量的正常ere eRy ery ery eRyy。设计了三个特定的目标：1）在红细胞生成期间对TF蛋白质组的绝对定量； 2）确定单个细胞中多个TF的抽象变化如何启动并逐渐增强细胞脂肪沿红细胞轨迹的决策； 3）促红细胞生成的计算分析，建模和验证。为了第一个目标，定量质谱法（MS）方法将用于测量来自健康供体的HSPC的体内红细胞生成期间TF蛋白的绝对水平。为了第二个目标，将使用完整的细胞和靶向MS蛋白质组学方法来估计单个细胞中的TF蛋白丰度，而其他单个细胞 - 组技术将用于测量离体促红细胞增多期间基因表达和TF基因组结合的变化。对于第三个目的，将使用TF蛋白丰度的测量和其他相关的 - 摩西数据数据来构建红细胞生成的TRN模型。根据我们的最新结果，将对在红细胞生成期间在转录控制中暗示的TF进行功能验证。该方法具有创新性，因为它使用了单细胞和大量蛋白质组学方法的新型组合来量化原代人类细胞中红细胞生成期间大量TF，并使用数据进行集成的TRN建模。拟议的研究很重要，因为它将照亮控制红细胞生成的复杂调节过程。最终，这种知识有可能指导新的治疗剂的设计，以重新建立β-丘脑血症患者的适当β-珠蛋白表达。