Analysis of genome wide transcriptional control in yeast

酵母全基因组转录调控分析 这个项目的长期目标是开发一个全面的和可预测的功能模型 真核细胞中的转录调控网络。我们试图理解全基因组转录 响应压力条件而触发的变化，就数量贡献而言 单个转录调控因子，包括序列特异性DNA结合转录因子以及 通过调节染色质结构来影响基因表达的转录调节因子。我们将使用 酵母作为一个模型系统，通过以下具体目标来解决这一领域的几个问题。首先，我们 将全面识别每个转录调节因子和染色质因子，这些因子可能调节 酵母菌的应激反应。这将通过酵母菌株的表型生长分析来完成。 哪个转录因子的功能受缺失或过表达的调节。转录因子缺失和 过表达菌株将通过在平板上点样和在液体中生长来筛选生长缺陷，在 三种不同的应激条件--热休克、营养饥饿和DNA损伤。第二，我们将确定 压力条件下关键监管机构的下游目标。我们将确定一个直接绑定目标 在应激反应期间的一组优先的转录调节因子， 采用染色质免疫沉淀结合芯片(CHIP-CHIP)。我们还将确定这些基因 通过在菌株中进行基因表达谱来主动和功能地调节这些因素 在已知这些转录因子的应激条件下，对选定的转录因子进行删除 这是必需的。最后，我们将整合我们的实验基因组数据，以建立一个预测性和因果调节 网络来解释所有酵母基因在胁迫下的转录调控。我们将使用贝叶斯 框架来模拟和重建这个转录调控网络，它将最低限度地包含 酵母中每个应激相关转录调控因子的靶标，并理想地解释每个 酵母基因在生理应激状态下的扰动。我们将使用这两个工具测试我们网络的预测能力 外部实验数据和基因功能注释。选定的预测监管关系 网络将通过定向实验进行验证。

Analysis of genome-wide transcriptional control in yeast The long term goal of this project is to develop a comprehensive and predictive model of a functional transcriptional regulatory network in a eukaryotic cell. We seek to understand genome-wide transcriptional changes that are triggered in response to stress conditions, in terms of the quantitative contributions of individual transcriptional regulators, including sequence-specific DNA binding transcription factors as well as transcriptional regulators that affect gene expression through modulating chromatin structure. We will use yeast as a model system to address several questions in this area through the following specific aims. First, we will comprehensively identify every transcriptional regulator and chromatin factor that potentially regulates stress responses in yeast. This will be accomplished through phenotypic growth assays of yeast strains in which transcription factor function is modulated by deletion or overexpression. Transcription factor deletion and overexpression strains will be screened for growth defects by spotting on plates and growth in liquid, under three different stress conditions - heat shock, nutrient starvation and DNA damage. Second, we will identify the downstream targets of key regulators under stress conditions. We will identify the direct binding targets of a prioritized set of transcription regulators during the stress responses that they are shown to be required for, using chromatin immunoprecipitation combined with microarrays (ChIP-chip). We will also identify the genes that are actively and functionally regulated by these factors, by carrying out gene expression profiling in strains deleted for the selected transcription factors, under the stress conditions that these factors are known to be required for. Finally, we will integrate our experimental genomic data to build a predictive and causal regulatory network to explain the transcriptional regulation of all yeast genes under stress. We will use a Bayesian framework to model and reconstruct this transcriptional regulatory network, which will minimally contain the targets of every stress-related transcriptional regulator in yeast, and ideally explain the regulation of every yeast gene under physiological stress perturbations. We will test the predictive ability of our network using both external experimental data and gene functional annotations. Selected predicted regulatory relationships in the network will be verified by directed experiments.