Functional Genomics of Dictyostelium

描述（由申请人提供）：本项目旨在了解控制网骨藻生长和多细胞发育的机制。在前一个项目期间，我们采用功能基因组学方法进行高通量突变表型分析，转录谱分析和计算建模，使我们能够为数百个基因进行功能推断。在接下来的五年里，我们将集中精力了解转录控制过程中的发展和细菌的识别，无论是在孤立的Dictyosteomaamoeastrum的生长过程中，并在其发展过程中的先天免疫反应的背景下。我们将交叉转录分析数据与生理数据，以提取有关协调细菌识别的遗传网络的信息，并表征所涉及的基因和途径。我们还将测试哪些监管机构负责的戏剧性的转录变化，伴随Dictyosteoblastoma的发展。我们将使用RNA测序（RNA-seq）和染色质免疫沉淀（ChlP-seq）来鉴定直接受这些转录因子调控的基因。我们将开发计算技术和综合数据挖掘来推断基因功能，并构建共识基因网络模型作为支架，在此基础上，我们可以提出额外的实验，并从其他实验中添加信息层。我们还建议在现代基于服务器的软件架构中实现新方法，具有可视化丰富的交互界面，这将使整个计划的数据分析透明，可由没有计算机科学背景的生物学家操作。这项工作将有助于建立阿米巴作为先天免疫研究的模型系统，从而开发可用于理解真核细胞对细菌反应的工具和技术。我们的菌株，数据和软件将免费提供给研究社区，并与dictyBase，主要的网骨藻社区资源和数据仓库很好地集成。

DESCRIPTION (provided by applicant): This Program Project is aimed at understanding the mechanisms that control growth and multicellular development in Dictyostelium. In the previous project period we took a functional genomics approach to high-throughput mutant phenotyping, transcriptional profiling, and computational modeling that allowed us to draw functional inferences for hundreds of genes. Over the next five years we will focus our efforts on understanding transcriptional control during development and bacterial recognition, both during the growth of solitary Dictyostelium amoebae and in the context of an innate immune response during their development. We will intersect transcriptional profiling data with physiological data to extract information about the genetic networks that coordinate bacterial recognition in Dictyostelium and characterize the genes and pathways involved. We will also test which regulators are responsible for the dramatic transcriptional changes that accompany Dictyostelium development. We will use RNA sequencing (RNA-seq) and Chromatin Immunoprecipitation (ChlP-seq) to identify genes that are directly regulated by these transcription factors. We will develop computational techniques and integrative data mining to infer gene function and to construct consensus gene network models for use as scaffolds upon which we can propose additional experiments and add layers of information from other experiments. We also propose to implement the new methods within modem server-based software architecture with visualization-rich interactive interfaces that will make the entire planned data analytics transparent and operable by biologists with no computer science background. This work will help establish the amoeba as a model system for the study of innate immunity, leading to the development of tools and techniques that can be applied to understanding the response of eukaryotic cells to bacteria. Our strains, data, and software will be freely available to the research community and well integrated with dictyBase, the primary Dictyostelium community resource and data warehouse.