Hit-and-Run transcription: The impact of transient interactions in dynamic gene regulatory networks that mediate rapid nutrient signaling

项目总结： 这项奖励利用了时间--转录的第四个维度，也是大部分未被探索的维度--来捕捉瞬变 基因调控网络(GRN)中的相互作用是重要的，但在体内被忽略。这是因为基因组- 捕捉转录因子(TF)靶相互作用的尺度方法有利于稳定结合和报告基因 在几秒钟内检测到瞬时TF-目标相互作用的研究，错过了GRN模型所需的全局响应。 我们的目标是通过实验捕获瞬变来填补我们对动态GRN的集体知识中的时间缺口 使用基于细胞的临时转移因子扰动分析(目标1和2)全局转移因子-靶相互作用，并评估 它们在预测未来时间点的基因表达方面的重要性(目标3)，这是系统生物学的主要目标。 我们对控制植物中氮(N)信号的时间GRN进行了建模，但我们的方法具有广泛的适用性。 我们开发了一种基于细胞的时间传递函数微扰分析方法，它捕获了瞬时传递函数靶 基因组范围内的相互作用；i)即使在没有可检测到的tf结合的情况下，也通过tf介导的基因调节，ii) 在受控的转铁蛋白核进入的几分钟内，以及iii)确定高度瞬时的转铁蛋白结合导致持续 通过亲和捕获新的mRNAs进行转录。我们发现：i)单个传输函数可以是稳定的或瞬时的 结合并诱导或抑制取决于其顺式上下文不同靶标集合，ii)瞬时Tf靶标 仅在细胞中捕获控制Planta中的早期N-反应，对于我们GRN中的两个主TF(bZIP1和NLP7)。这 全基因组数据支持打了就跑的转录模型，在该模型中，TF的命中可以启动稳定的 转录复合体，包括招募TF伙伴，使转录能够在启动后继续 Tf不再绑定，该运行。这可能允许少量的转铁蛋白分子迅速影响大量的 通过催化作用获得目标基因。我们的研究被引用并影响了人们对瞬变的思考 跨酵母、干细胞的转录机制，并被用来解释新发现的瞬时 果蝇中Zelda/Bigid与报告基因的结合。在这里，我们部署了实验和计算 测试概念创新的普及性和活体意义的创新--瞬时击中和- 在GRN中运行交互。我们的实验性创新包括：i)对所有人的肇事逃逸活动进行检测 拟南芥中的70个转铁蛋白家族，使用基于细胞的靶向分析的高通量版本 发表，二)使用时间序列生物素芯片和DAMID捕获转移因子-目标相互作用的新方法， 在暂态转运蛋白-靶相互作用上留下DNA甲基化标记，由初步数据支持(目标1和2)。 我们的计算创新包括：i)ConnectTF，一个整合Tf-DNA结合和RNA-Seq的平台 数据和确定候选肇事逃逸TF，以及评估瞬时TF的内在相关性的方法- GRN中的目标交互，例如II)我们新发布的网络漫游方法，以及III)OutPredict，一种新的 基于时间的方法预测未来时间点的基因表达(目标3)。我们的实验和计算 方法是广泛适用的，我们的结果与环境氮使用影响人类健康有关。

Project Summary: This grant exploits TIME - the 4th and largely unexplored dimension of transcription - to capture transient interactions in gene regulatory networks (GRNs) that are important, but missed, in vivo. This is because genome- scale methods to capture transcription factor (TF) target interactions favor stable binding, and reporter gene studies which detect transient TF-target interactions in seconds, miss global responses needed for GRN models. We aim to fill the time-gap in our collective knowledge of dynamic GRNs by experimentally capturing transient TF-target interactions globally using a cell-based temporal TF perturbation assay (Aims 1 & 2), and evaluate their importance in forecasting gene expression at future time-points (Aim 3), a main goal of systems biology. We model temporal GRNs controlling nitrogen (N)-signaling in plants, but our approaches are broadly applicable. We exploit a cell-based assay for temporal TF perturbation, TARGET, which captures transient TF-target interactions genome-wide; i) by TF-mediated gene regulation even in the absence of detectable TF-binding, ii) within minutes of controlled TF nuclear entry, and iii) identifies highly transient TF-binding leading to sustained transcription by affinity-capture of de novo mRNAs. We discovered that i) a single TF can stably or transiently bind to, and induce or repress, distinct sets of targets depending on their cis-context, ii) that transient TF-targets captured only in cells control early N-responses in planta, for two master TFs in our GRNs (bZIP1 & NLP7). This genome-wide data supports a Hit-and-Run transcription model, where a TF Hit can initiate a stable transcriptional complex, including recruitment of TF partners, enabling transcription to continue after the initiating TF is no longer bound, the Run. This could allow a small number of TF molecules to rapidly affect a large number of target genes by acting catalytically. Our studies have been cited and influenced thinking of transient transcription mechanisms across yeast, stems cells, and were invoked to explain the new discovery of transient binding of Zelda/Bicoid to a reporter gene in Drosophila. Herein, we deploy experimental and computational innovations to test the pervasiveness and in vivo significance of a conceptual innovation - transient Hit-and- Run interactions in GRNs. Our experimental innovations include; i) Assays for Hit-and-Run activity across all 70 TF families in Arabidopsis, using a higher throughput version of the cell-based TARGET assay we recently published, ii) new methods to capture TF-target interactions using time-series biotin-ChIP and DamID, which leaves DNA methylation marks on transient TF-target interactions, supported by preliminary data (Aims 1 & 2). Our computational innovations include: i) ConnectTF, a platform to integrate TF-DNA binding and RNA-seq data and identify candidate Hit-and-Run TFs, and approaches to assess the in planta relevance of transient TF- target interactions in GRNs, such as ii) our newly published Network Walking method, and iii) OutPredict, a new time-based method to forecast gene expression at future time-points (Aim 3). Our experimental & computational approaches are broadly applicable and our results are relevant to environmental N-use affecting human health.