A quantitative analysis of dynamic signal processing by transcription factors

描述(由申请人提供)：细胞通过细胞内信号通路对广泛的环境信号做出反应。越来越多的研究表明，细胞通过控制信号分子活动的时间动态来传递环境信息。然而，理解这些动态模式是如何被解码以影响细胞反应的，仍然是一个具有挑战性的目标。蛋白激酶A(PKA)是一种高度保守的原型激酶，通过转录程序调节许多细胞行为，如生长和抗逆性。在对环境刺激的反应中，PKA表现出不同的信号活动动态。PKA活性动态调节的缺陷可能会导致灾难性的疾病，如癌症和心脏病。为了了解PKA动力学的解码机制和功能，我们最近在酿酒酵母中开发了一个合成系统，能够直接和精确地控制细胞内的信号活动。该系统能够揭示信号动力学和下游响应之间的因果关系和直接的机制联系，因此是建立信号系统机制模型的有用工具。在拟议的研究中，这个动态控制系统将与单细胞成像、高通量微流体和计算建模相结合，以产生时间控制的PKA输入，并定量地研究这些信号动力学如何被解码，以通过动态调节的单个转录因子(TF)、通过具有不同动力学的两个平行转录因子以及通过转录网络基序来影响基因表达反应。模型分析表明，靶基因根据其启动子的动力学特性对TF输入的动态进行解码。在目标1中，将确定支配基因对转铁蛋白动力学反应的分子过程的动力学，并将这些信息用于开发详细的转录动力学模型。这些模型将在目标2中进一步使用和改进，以分析两个看似多余的转录因子如何明显地处理信号动力学，以及它们如何促进转录反应的动态多样性。单细胞成像分析将用于检验模型结果。在目标3中，我们将建立来自先前目标的模型，并研究由多个TF和靶基因组成的网络基序如何处理和解码信号动力学。高通量微流控平台将用于跟踪单个细胞中每个基序成分的丰度和亚细胞定位。基于单细胞数据，将进行建模分析，以分析不同的母题如何使动态响应多样化，从而差异地解码信号输入的时间模式。该项目的完成将使人们对信号动力学的解码机制和功能相关性有一个定量的了解，并将为计算导向的人类疾病的药物治疗奠定科学基础。

DESCRIPTION (provided by applicant): Cells respond to a wide range of environmental cues through intracellular signaling pathways. An increasing number of studies revealed that cells transmit environmental information by controlling the temporal dynamics of activities of signaling molecules. However, understanding how these dynamic patterns are decoded to influence cellular responses remains a challenging goal. Protein kinase A (PKA) is a highly conserved prototypic kinase that regulates many cellular behaviors, such as growth and stress resistance, through transcriptional programs. In response to environmental stimuli, PKA displays various dynamics of signaling activity. Defects in dynamic regulation of PKA activity can lead to disastrous diseases, such as cancer and heart disease. To understand the decoding mechanisms and functions of PKA dynamics, we recently developed a synthetic system in yeast S.cerevisiae that enables direct and precise control of intracellular signaling activities. This system is capable of revealing causal relationships and direct mechanistic connections between signaling dynamics and downstream responses, and hence is a useful tool for developing mechanistic models of signaling systems. In the proposed research, this dynamic control system will be integrated with single-cell imaging, high-throughput microfluidics and computational modeling to produce temporally controlled PKA inputs and to quantitatively investigate how these signaling dynamics are decoded to influence gene expression responses via a single transcription factor (TF) that is dynamically regulated, via two paralogous TFs with distinct dynamics, and via transcriptional network motifs. Modeling analysis suggests that target genes decode dynamics of TF input based on the kinetic properties of their promoters. In Aim 1, the kinetics of molecular processes that govern gene responses to TF dynamics will be determined and this information will be used to develop detailed kinetic models of transcription. These models will be further used and improved in Aim 2 to analyze how two seemingly redundant TFs can distinctly process signaling dynamics and how they contribute to the dynamic diversity of transcriptional responses. Single-cell imaging analysis will be used to test the model results. In Aim 3, we will build on the models from previous aims and investigate how network motifs, composed by multiple TFs and target genes, process and decode signaling dynamics. A high- throughput microfluidic platform will be used to track the abundance and subcellular localization of each motif components in single cells. Based on the single-cell data, modeling analysis will be conducted to analyze how distinct motifs diversify the dynamic responses to differentially decode temporal patterns of signaling inputs. The completion of this project will lead to a quantitative understanding about the decoding mechanisms and functional relevance of signaling dynamics and will lay the scientific foundation for computationally-guided pharmacological treatments of human diseases.