Comparative Analysis of Genetic and Physical Interaction Networks in Yeasts

描述（由申请人提供）：比较相互作用图谱基于蛋白质复合物和信号通路在不同物种或类型的相互作用网络中的保守性来解析蛋白质复合物和信号通路。它是一种新兴的方法，像比较基因组学一样，为理解细胞功能提供了强大的工具。网络比较已被用于确定许多蛋白质的功能作用，它提供了深入了解基因组中的突变如何有助于新功能和表型的进化。目前实现这些目标的障碍是缺乏适当进化距离的高覆盖率交互图，以进行网络比较。 为了解决这一缺点，本项目的主要目标是获得匹配的高密度物理和遗传相互作用的地图在模式生物粟酒裂殖酵母和酿酒酵母。这些数据的使用将最终帮助解决以下问题：物理、遗传和转录相互作用网络的结构如何密切地反映潜在基因组序列的变异？物理相互作用组、遗传通路、转录网络和蛋白质序列水平的突变对新细胞功能的进化有什么相对贡献？与S. pombe和S.也可以处理酿酒酵母。S.粟酒裂殖吸虫的生理学与哺乳动物的相似之处多于粟酒裂殖吸虫。酿酒酵母，包括内含子/外显子剪接，染色体结构和RNA干扰机制。事实上，S. pombe和S.酿酒酵母是相当古老的（4.2亿年），使保守的相互作用地图推广到大部分的真核细胞谱系。将我们最初的相互作用绘图工作集中在最有可能形成S. pombe和S.在酿酒酵母生理学中，我们将筛选一组靶向的~400种激酶和转录调节因子之间的相互作用。这套地图将充分限制我们的范围，以便可以在五年的时间内以合理的资金水平获得高覆盖率的地图。 将使用上位性表型、使用亲和纯化随后串联质谱的蛋白质-蛋白质相互作用以及使用全基因组染色质免疫沉淀试验的转录因子/启动子结合相互作用来测量成对遗传相互作用。与此同时，我们将开发一套配套的生物信息学方法，对酵母相互作用网络进行综合和比较分析。生物信息学研究将解决：[1]数量遗传和物理相互作用之间的相互作用模型; [2]跨物种整合网络的对齐;[3]在不同程度的数据整合下，预测和转移物种内和物种间的相互作用。 组装转录调节因子和激酶的网络将作为一个试点，建立网络整合和比较的基本原则，然后再进行大规模的努力，全面映射裂变酵母以及高等真核生物。这将涉及两名主要调查员Krogan和Ideker之间的密切协调。它将加入两个加州大学校园以及两个加州研究所，加州IT 2和QB 3，这是承诺空间容纳拟议中的项目在圣地亚哥和弗朗西斯科地区。

DESCRIPTION (provided by applicant): Comparative interaction mapping resolves protein complexes and signaling pathways on the basis of their conservation across different species or types of interaction network. It is an emerging methodology which, like comparative genomics, provides a powerful tool for understanding cellular function. Network comparison has been used to identify the functional roles of many proteins, and it offers insight into how mutations in the genome contribute to the evolution of new functions and phenotypes. A current hurdle towards these goals is the lack of high-coverage interaction maps at the appropriate evolutionary distances to enable network comparison. To address this shortcoming, the major goal of this project is to obtain matching sets of highdensity physical and genetic interaction maps across the model organisms Schizosaccharomyces pombe and Saccharomyces cerevisiae. Use of these data will ultimately help resolve the following questions: How closely do the architectures of the physical, genetic, and transcriptional interaction networks reflect variation in the underlying genomic sequence?; What relative contributions do changes in the physical interactome, genetic pathways, transcriptional networks, and mutations at the protein sequence level make to the evolution of new cellular functions? Specific hypotheses directly related to the physiologies of S. pombe and S. cerevisiae can also be addressed. Many aspects of S. pombe physiology bear more in common with mammals than does S. cerevisiae, including intron/exon splicing, chromosomal architecture, and RNA interference machinery. In fact, the last common ancestor of S. pombe and S. cerevisiae is quite ancient (420 mya), making the conserved interaction map generalizable to large parts of the eukaryotic lineage. To focus our initial interaction mapping efforts on the regulatory machinery most likely to form the basis for the similarities and differences between S. pombe and S. cerevisiae physiology, we will screen interactions among a targeted set of ~400 kinases and transcriptional regulators. This set will limit our scope sufficiently such that high coverage maps can be obtained at reasonable funding levels within a five-year time frame. Pair-wise genetic interactions will be measured using epistatic phenotyping, protein-protein interactions using affinity purification followed by tandem mass spectrometry, and transcription factor / promoter binding interactions using genome-wide chromatin immunoprecipitation assays. In parallel, we will develop a companion suite of bioinformatic methods to perform integrative and comparative analysis of the yeast interaction networks. Bioinformatic research will address: [1] Models of interplay between quantitative genetic and physical interactions; [2] Alignment of the integrated networks across species; and [3] Prediction and transfer of interactions within and across species, at varying degrees of data integration. Assembling the network of transcriptional regulators and kinases will serve as a pilot for establishing basic principles of network integration and comparison prior to embarking on larger-scale efforts to comprehensively map fission yeast as well as higher eukaryotes. It will involve close coordination among the two principal investigators Krogan and Ideker. It will join two University of California campuses as well as two California institutes, Cal-IT2 and QB3, which are committing space to house the proposed project in the San Diego and San Francisco areas.