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

项目摘要： 这项资助利用了时间-转录的第四维，也是大部分未被探索的维度-来捕捉瞬时的 基因调控网络（GRNs）中的相互作用是重要的，但错过了，在体内。因为基因组- 捕获转录因子（TF）靶向相互作用的规模方法有利于稳定结合， 在几秒钟内检测瞬时TF-靶相互作用的研究错过了GRN模型所需的全局响应。 我们的目标是填补我们的集体知识的动态GRNs的实验捕捉瞬态的时间差距 使用基于细胞的时间TF扰动测定法（目的1和2），全面评估TF-靶标相互作用，并评估 它们在预测未来时间点的基因表达（目标3）中的重要性，这是系统生物学的主要目标。 我们对植物中控制氮（N）信号的时间GRNs进行建模，但我们的方法具有广泛的适用性。 我们利用一种基于细胞的检测时间TF扰动的方法，TARGET，它捕获瞬时TF靶点。 全基因组相互作用; i）即使在不存在可检测的TF结合的情况下，也通过TF介导的基因调节，ii） 在受控TF核进入的几分钟内，和iii）鉴定高度瞬时的TF结合，导致持续的 通过从头mRNA的亲和捕获进行转录。我们发现i）单个TF可以稳定地或瞬时地 结合，并诱导或抑制，不同的目标集，这取决于它们的顺式背景，ii）瞬时TF-靶 仅在细胞中捕获控制植物中的早期N-反应，对于我们的GRNs中的两个主TF（bZIP 1和NLP 7）。这 全基因组数据支持打了就跑的转录模型，其中TF命中可以启动稳定的转录。 转录复合物，包括TF伴侣的招募，使转录在启动后继续进行。 TF不再绑定，该运行了。这可以让少量的TF分子迅速影响大量的TF分子。 通过催化作用对靶基因产生影响。我们的研究已经被引用并影响了对瞬态的思考 转录机制在酵母，干细胞，并被调用来解释新发现的瞬时 Zelda/Bicoid与果蝇报告基因的结合。在这里，我们部署实验和计算 创新，以测试概念创新的普遍性和体内意义-瞬时命中和- 在GRN中运行交互。我们的实验创新包括：i）对所有车辆的肇事逃逸活动进行测定 最近，我们使用基于细胞的TARGET测定的更高通量版本，对拟南芥中的70个TF家族进行了研究 ii）使用时间序列生物素-ChIP和DamID捕获TF-靶标相互作用的新方法， 在瞬时TF-靶标相互作用上留下DNA甲基化标记，得到初步数据的支持（目的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 数据和识别候选的打了就跑TF，以及评估瞬时TF在体内相关性的方法， 在GRNs的目标相互作用，如ii）我们新发布的网络步行方法，和iii）OutPredict，一个新的 基于时间的方法来预测未来时间点的基因表达（目的3）。我们的实验和计算 方法是广泛适用的，我们的研究结果是相关的环境氮的使用影响人类健康。

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.