Uncovering the molecular basis for dynamic regulatory networks in plants

 描述(由申请人提供)：了解基因调控网络(GRN)如何使生物体适应其环境的变化是生物系统的核心。然而，这种网络通常被视为一个静态的相互作用网络，而实际上它们是高度流动和动态的，这使得它们能够整合多种营养和环境信号，并产生快速、微调的反应。我的最终目标是捕捉动态监管网络并为其建模。在这个项目中，我将使用新的实验和计算方法来确定GRN中难以捉摸的瞬时转录因子(TF)靶标相互作用，并询问它们在植物对氮(N)信号的快速反应中的作用。这些研究的原则证明是碱性亮氨酸拉链1(BZIP1)，它 在植物和其他真核生物中调节营养信号，但这种方法可以应用于任何生物体中的任何转铁蛋白。我的研究将利用一种基于细胞的系统，它将使我能够在时间上干扰根细胞中传递的Tf和N信号--N信号是第一次被察觉的--并在Tf核进入后的几分钟内捕获Tf-靶相互作用[2]。在目标1中，将使用一种新的DNA甲基化指纹技术，结合时间序列转录组分析和染色质免疫沉淀，在分离的根细胞中捕获瞬时的TF-靶相互作用。接下来，在目标2中，在分离的细胞中捕获的这些瞬时的TF-靶相互作用将被连接到整个植物中发生的下游反应，使用一种名为“网络行走”的组合基因组和网络推理方法。最后，在目标3中，将验证在N-响应网络中确定的重要转录前馈基序，并评估它们对氮信号的影响。本研究对提高氮肥利用率，减少地下水氮肥污染对环境和人类健康的危害具有重要意义。更广泛地说，实验和计算相结合的方法通常适用于快速分析跨真核生物的动态基因调控网络。

 DESCRIPTION (provided by applicant): Understanding how gene regulatory networks (GRNs) allow organisms to adapt to changes in their environment is at the heart of biological systems. However, such networks are typically visualized as a static web of interactions when in reality they are highly fluid and dynamic, which allows them to integrate multiple nutrient and environmental signals and generate a rapid, fine-tuned response. My ultimate goal is to capture and model dynamic regulatory networks. In this project, I will use new experimental and computational approaches to identify the elusive transient transcription factor (TF) target interactions in GRNs and interrogate their role in mediating rapid responses to nitrogen (N) signals in plants. The proof-of-principle for these studies is basic leucine zipper 1 (bZIP1) which mediates nutrient signals in plants and other eukaryotes, but the approach can be applied to any TF in any organism. My studies will exploit a cell-based system that will enable me to temporally perturb both a TF and the N signal it transduces in root cells - where the N-signal is first perceived - and to capture TF-target interactions within minutes of TF nuclear entry [2]. In Aim 1, transient TF- target interactions will be captured in isolated root cells using a new DNA-methylation fingerprinting technique in combination with time-series transcriptome profiling and chromatin immunopreciptiation. Next, in Aim 2, these transient TF-target interactions captured in isolated cells will be connected to downstream responses that occur in the whole plant, using a combined genomic and network inference approach called "Network Walking". Finally, in Aim 3, important transcriptional feed-forward motifs identified in the N-response network will be validated, and their impact on nitrogen signaling will be assessed. This study will have implications on N-use efficiency and reducing environmental and human health hazards of N-fertilizer contamination of ground water. More broadly, the combined experimental and computational approach is generally applicable to rapidly analyze dynamic gene regulatory networks across eukaryotes.