Expression2Kinases: mRNA Profiling Linked to Multiple Upstream Regulatory Layers

描述（由申请人提供）：全基因组mRNA分析提供了不同实验条件下哺乳动物细胞整体状态的快照，如患病与正常或药物与模拟治疗细胞状态。然而，由于测量是以mRNA水平定量变化的形式进行的，因此这些实验数据并不能直接理解导致所观察到的变化的调控上游分子机制。在不同的实验条件下或在不同的组织中识别负责基因表达变化的潜在细胞信号调节机制一直是许多计算系统生物学努力的焦点。最流行的方法包括基因本体或途径富集分析，以及从mRNA表达数据中对网络进行逆向工程。然而，这些方法通常假设差异表达的基因产生通路和功能模块，这在高等真核生物中并不总是正确的。在这里，我们提出了另一种合理的方法，称为Expression2Kinases，来识别和排名转录因子，染色质修饰剂，蛋白质复合物和蛋白质激酶可能负责观察到的基因表达变化。通过结合ChIP-seq和ChIP-chip实验数据、公开数据库中报道的蛋白-蛋白相互作用以及从文献中收集的激酶-蛋白磷酸化反应，我们可以根据基因表达的全基因组变化确定上游调控因子并对其进行排序。这个想法是推断转录因子和染色质调节因子负责

DESCRIPTION (provided by applicant): Genome-wide mRNA profiling provides a snapshot of the global state of mammalian cells under different experimental conditions such as diseased vs. normal or drug vs. mock treatment cellular states. However, since measurements are in the form of quantitative changes in mRNA levels, such experimental data does not provide direct understanding of the regulatory upstream molecular mechanisms responsible for the observed changes. Identifying potential cell signaling regulatory mechanisms responsible for changes in gene expression under different experimental conditions or in different tissues has been the focus of many computational systems biology efforts. Most popular approaches include gene ontology or pathway enrichment analyses, as well as reverse engineering of networks from mRNA expression data. However, these methods often assume that differentially expressed genes give rise to pathways and functional modules which is not always true in higher eukaryotes. Here we propose an alternative rational approach, called Expression2Kinases, to identify and rank transcription factors, chromatin modifiers, protein complexes, and protein kinases that are likely responsible for observed changes in gene expression. By combining data from ChIP-seq and ChIP-chip experiments, protein-protein interactions reported in publicly available databases, and kinase-protein phosphorylation reactions collected from the literature, we can identify and rank upstream regulators based on genome-wide changes in gene expression. The idea is to infer the transcription-factors and chromatin regulators responsible for changes in gene-expression; then use protein-protein interactions to "connect" the identified factors to build transcriptional complexes involving the factors; then use kinase-protein phosphorylation reactions to identify and rank candidate protein kinases that most likely regulate the formation of the identified transcriptional complexes. We plan to validate this method with phosphoproteomics data, data from drug perturbations followed by genome-wide gene expression, RNAi screens, as well as through literature-based text-mining approaches. The project will produce several high quality datasets, web-based software, new algorithms, and robust lists of transcription-factors, histone modifiers, and kinase rankings likely responsible fo mammalian cell regulation. The approach will be experimentally tested in several collaborative projects mainly exploring regulation of differentiating stem and iPS cells. The databases, software tools and algorithms developed for this project will advance drug target discovery and help in unraveling drug mechanisms of action.