Machine Vision Analysis of C. Elegans Phenotypic Patterns

描述(申请人提供)：复杂的遗传网络是人类疾病和健康的基础。构建遗传网络现在是酵母和培养的哺乳动物细胞等简单细胞的标准技术。多细胞生物体的网络推理特别有希望，但一个挑战是将网络解析成功能路径，而不仅仅是连接的图，第二个挑战是分析网络的复杂表型，如神经元功能和行为。我们的目标是以线虫为模型，学习如何完成这项任务，同时生成一个将向人类遗传学提供信息的网络。特别是，我们将继续利用我们的半自动运动分析系统(WormTracker)来获得大量基因的表型图谱。将使用可用的功能丧失突变来询问基因。被检查的基因将包括所有相关的神经元基因，以及编码染色质修饰蛋白和转录因子的基因。转录调节蛋白或染色质修饰蛋白与神经元效应基因的计算聚类将推断基因之间的调控关系。除了食物上的运动外，我们还会在爬行和游泳时获得食物上的运动分数。我们将对表型进行分类以推断遗传模块，并使用其他可用的基因组规模数据(如基因表达数据)扩展这些模块。为了获得药物-基因网络，我们将描述一组具有代表性的药物，并将它们与基因表型图谱进行比较。我们将通过测试特定的药物-基因相互作用来测试药物-基因网络的预测。为了完善遗传网络，我们将开发额外的表型图谱方法，并应用于基因、药物和基因-药物相互作用，将网络划分为表型空间区域。这些分析将包括使用微流控设备对咽泵的速度和变异进行定量、自动化分析，建立对男性尾部姿势和毛刺延长的药理效应分析，以采样对更复杂的男性神经系统的遗传影响，以及监测感官反应的化学诱导剂和驱避剂小组。我们将通过将数量行为表型的广泛数据集与允许遗传网络推断的现有信息(表达数据、体外结合、基因本体论注释、染色质免疫沉淀数据等)相结合来利用我们的结果。从WormBase导入。硬件建设的软件和协议将从实验室网站免费获得。

DESCRIPTION (provided by applicant): Complex genetic networks underlie human disease and health. The construction of genetic networks is now a standard technique in simple cells such as yeast and cultured mammalian cells. Network inference for multicellular organisms is promising especially but one challenge is to parse the network into functional pathways as opposed to just connected graphs, and a second challenge is to analyze networks for complex phenotypes such as neuronal function and behavior. Our goal is to use C. elegans as a model to learn how to accomplish this task, meanwhile generating a network that will inform human genetics. In particular, we will continue to exploit our semi-automated locomotion analysis system (WormTracker) to obtain a phenotypic profile for a large set of genes. Genes will be interrogated using available loss-of- function mutations. The genes examined will include all relevant neuronal genes, as well as genes that encode chromatin modifying proteins and transcription factors. Computational clustering of transcriptional regulators or chromatin modifying proteins with neuronal effector genes will infer regulatory relationships among genes. In addition to locomotion on food, we will also score locomotion off food, and both during crawling and swimming. We will cluster the phenotypes to infer genetic modules, and expand these modules using other available genome- scale data such as gene expression data. To obtain a drug-gene network, we will profile a representative set of drugs and compare them to gene phenotypic profiles. We will test predictions of the drug-gene network by testing particular drug-gene interactions. To refine the genetic network, we will develop additional phenotypic profiling methods, and apply to genes, drugs and gene-drug interaction to split the network into regions of phenotype space. These assays will include quantitative, automated analysis of the rate and variation in pharyngeal pumping using microfluidic devices, established assays for pharmacological effects on male tail posture and spicule protraction to sample genetic effects on the more complex male nervous system, and panels of chemoattractants and repellants to monitor sensory responses. We will leverage our results by integrating what will an extensive data set on quantitative behavioral phenotypes with existing information that allow genetic network inference (expression data, in vitro binding, Gene Ontology annotations, Chromatin immunoprecipitation data, etc.) imported from WormBase. Software and protocols for hardware construction will be freely available from laboratory websites.