Reconstruction and Modeling of Networks Involved in Cardiomyocyte Differentiation

描述(申请人提供)：心脏前体细胞是如何分化成心肌细胞的？在过去的二十年里，这个问题一直是无数调查的主题。虽然这些实验已经确定了在分化的一个或多个阶段参与信号或转录调节通路的几个分子角色，但导致心肌发生的细胞网络的全面和定量的图景尚未出现。这是我们提案的一个主要目标。我们将从启动特定阶段分化过程的粗粒度图景的遗留知识开始，并进行信息丰富的分析，以获得有关参与者和信号模块的定性和定量知识。第一组检测将结合信号模块知识和模块特异性抑制剂的引入，以初步了解哪些模块在分化的哪些阶段被激活或抑制。随后将进行磷酸化蛋白质组学分析，以确定特定的磷酸化级联反应，并为我们提供更详细的信号网络图景。我们将使用特定阶段的基因表达测量来进一步扩展这一点，并分析整合的数据集，以获得更多导致心肌发生的细粒度途径。最重要的是，我们将使用细胞内钙离子的时空测量来开发信号网络的量化模型，该模型将有助于映射前体细胞激活心肌发生时的输入与响应。这种定量的系统生物学方法有助于理解再生医学中最重要的过程之一，即胚胎干细胞形成心脏的过程，它有可能首先提供对信号模块的组合复杂性的见解，这些信号模块在细胞通过心肌生成程序进行过程中运行。这将为我们提供有关有效地将ESCs分化为心肌细胞所需的特定触发器和激活剂的重要信息。其次，我们将开发一个部件列表和每个分化阶段事件的详细网络地图，以建立一个关于分化的系统生物学观点。第三，也是最重要的，我们将提供将输入映射到响应的量化框架，即心肌发生的表型。最后，识别与我们的实验相关的配体和分子将提供有趣的治疗靶点。该项目以一种特别协同的方式将干细胞生物学、基因表达和定量系统生物学的专家聚集在一起，并将为社区提供宝贵的生物医学数据、模型和假设。心脏病是人类最常见的先天缺陷。这项建议的成功完成将极大地帮助我们目前对心脏发育和疾病的了解，并在较长期内突出未来研究的特定分子途径，从而成为未来心脏药物治疗的潜在目标。

DESCRIPTION (provided by applicant): How are cardiac progenitor cells primed to differentiate into cardiomyocytes? This question has been the subject of numerous investigations over the past two decades. While, these experiments have identified several molecular players that are involved in signaling or transcription regulation pathways at one or more stages of differentiation, a comprehensive and quantitative picture of the cellular networks leading to cardiomyogenesis is yet to emerge. This is a primary goal of our proposal. We will begin with a legacy knowledge of the coarse-grained picture of initiation of stage-specific differentiation processes and conduct information-rich assays to obtain qualitative and quantitative knowledge of the players and signaling modules involved. The first set of assays will combine signaling module knowledge with introduction of module-specific inhibitors to get a first glimpse at which modules are activated or repressed at which stages of differentiation. This will be followed by phosphoproteomic analysis to identify specific phosphorylation cascades and provide us a more detailed picture of the signaling networks. We will expand this further using stage-specific gene- expression measurements and analyze the integrated sets of data to obtain more fine-grained pathways that lead to cardiomyogenesis. Most importantly, we will use spatio-temporal measurements of intracellular calcium to develop a quantitative model of the signaling networks that will help map input to response in activation of precursor cells towards cardiomyogenesis. This quantitative systems biology approach towards understanding one of the most important processes in regenerative medicine, that of cardiac formation from ESCs has the potential first to provide insights into the combinatorial complexity of signaling modules that operate as cells progress through the cardiomyogenic program. This will provide us vital information on specific triggers and activators needed to efficiently differentiate ESCs into cardiomyocytes. Second, we will develop both a parts list and a detailed network map of events at each stage of differentiation to build a systems biology perspective on differentiation. Third and most important, we will provide the quantitative framework for mapping input to response, i.e. phenotype in cardiomyogenesis. Finally, identification of ligands and molecules associated with our experiments will provide interesting therapeutic targets. The project brings together experts in stem cell biology, gene expression and quantitative systems biology in an exceptionally synergistic manner and will provide the community with invaluable data, models and hypotheses for biomedicine. Heart disease is the most common birth defect in humans. Successful completion of this proposal would greatly aid in our current understanding of heart development and disease and in the longer term highlight specific molecular pathways for future studies and thus, potential targets for future cardiac drug therapies.