The Ancient Genetic Networks of Obesity: Whole-Animal Automated Screening for Conserved Fat Regulators
The Ancient Genetic Networks of Obesity: Whole-Animal Automated Screening for Conserved Fat Regulators
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DOI：
10.1007/978-1-4939-7847-2_10
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发表时间：
2018-01-01
期刊：
影响因子：
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通讯作者：
O'Rourke, Eyleen J.
中科院分区：
文献类型：
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作者：
Ke, Wenfan;Drangowska-Way, Anna;O'Rourke, Eyleen J.
秀丽隐杆线虫是第一个也是唯一一个通过简单地喂养标准实验室饮食大肠杆菌（携带 RNA 干扰 (RNAi) 表达结构）即可实现全身基因敲除的后生动物模型。 RNAi 处理的简单性、小尺寸和线虫的快速繁殖率使我们能够在野生型、突变体或其他转基因线虫中进行全动物高通量遗传筛选。此外，超过65%的线虫基因在包括人类在内的哺乳动物中是保守的。特别是，线虫代谢途径高度保守，这支持在这种遗传易处理的模型系统中研究肥胖等复杂疾病。在本章中，我们提出了一个详细的自动化高通量全动物 RNAi 筛选方案，以确定饮食诱导和基因驱动的肥胖线虫中促进肥胖的途径。我们描述了一种优化的高内涵筛选方案，用于大规模对整个动物的脂肪质量和体脂肪分布进行评分。我们提供优化的管道，在超级计算机集群的环境中使用开源 CellProfiler 平台自动对表型进行评分。此外，我们提出了优化从自动显微镜到可搜索数据库的信息工作流程的指南。这里描述的方法能够揭示基因-基因和基因-环境相互作用的整个网络，这些相互作用定义了这个经过验证的人类疾病模型中的代谢健康或疾病状态，但类似的原理也可以应用于其他疾病模型。
Caenorhabditis elegans is the first and only metazoan model that enables whole-body gene knockdown by simply feeding their standard laboratory diet, E. coli, carrying RNA interference (RNAi)-expressing constructs. The simplicity of the RNAi treatment, small size, and fast reproduction rate of C. elegans allow us to perform whole-animal high-throughput genetic screens in wild-type, mutant, or otherwise genetically modified C. elegans. In addition, more than 65% of C. elegans genes are conserved in mammals including human. In particular, C. elegans metabolic pathways are highly conserved, which supports the study of complex diseases such as obesity in this genetically tractable model system. In this chapter, we present a detailed protocol for automated high-throughput whole-animal RNAi screening to identify the pathways promoting obesity in diet-induced and genetically driven obese C. elegans. We describe an optimized high-content screening protocol to score fat mass and body fat distribution in whole animals at large scale. We provide optimized pipelines to automatically score phenotypes using the open-source CellProfiler platform within the context of supercomputer clusters. Further, we present a guideline to optimize information workflow from the automated microscope to a searchable database. The approaches described here enable unveiling the whole network of gene-gene and gene-environment interactions that define metabolic health or disease status in this proven model of human disease, but similar principles can be applied to other disease models.