Expression2Kinases: mRNA Profiling Linked to Multiple Upstream Regulatory Layers

描述(由申请人提供)：全基因组信使核糖核酸图谱提供了哺乳动物细胞在不同实验条件下的全球状态的快照，例如疾病与正常或药物与模拟治疗的细胞状态。然而，由于测量是以mRNA水平的定量变化的形式进行的，这样的实验数据并不能直接了解导致所观察到的变化的调控上游分子机制。识别在不同实验条件下或不同组织中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论或途径丰富分析，以及根据mRNA表达数据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Expression2Kinase，来识别和排序可能导致观察到的基因表达变化的转录因子、染色质修饰物、蛋白质复合体和蛋白激酶。通过结合CHIP-SEQ和CHIP-CHIP实验的数据，公开可用的数据库中报告的蛋白质-蛋白质相互作用，以及从文献中收集的激酶-蛋白质磷酸化反应，我们可以根据基因表达的全基因组范围的变化来识别和排序上游调控因子。这个想法是为了推断转录因子和染色质调节器负责 我们的研究主要集中在基因表达的变化上；然后利用蛋白质-蛋白质相互作用将识别的因子“连接”起来，构建与这些因子相关的转录复合体；然后使用激酶-蛋白质磷酸化反应来识别和排列最有可能调节已识别的转录复合体的形成的候选蛋白激酶。我们计划用磷蛋白质组学数据、药物扰动数据、全基因组基因表达、RNAi筛选以及基于文献的文本挖掘方法来验证这种方法。该项目将产生几个高质量的数据集、基于网络的软件、新的算法，以及可能负责哺乳动物细胞调控的转录因子、组蛋白修饰物和激酶排名的强大列表。该方法将在几个合作项目中进行实验测试，主要是探索干细胞和iPS细胞分化的调节。为该项目开发的数据库、软件工具和算法将促进药物靶标的发现，并有助于揭示药物的作用机制。 与公共卫生相关：该项目提出了一种新的Rational管道，以识别和排序上游调控因素，这些上游调控因素负责在基因组范围内从哺乳动物细胞收集的基因表达变化，以及预测FDA批准的药物组合如何用于影响基因表达变化的方法。该方法有可能快速推进药物靶点的发现，建议药物重新定位策略，并有助于解开药物的作用机制，并将主要应用于通过实验合作研究干细胞分化和iPS重新编程。

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. PUBLIC HEALTH RELEVANCE: The project presents a new rational pipeline to identify and rank upstream regulators that are responsible for observed changes in gene expression collected at the genome-wide scale from mammalian cells as well as method to predict how combinations of FDA approved drugs could be used to affect changes in gene expression. The approach has the potential to rapidly advance drug target discovery, suggest drug repositioning strategies, and help in unraveling drug mechanisms of action, and will be mainly applied to study stem cell differentiation and iPS reprogramming through experimental collaborations.