Department of Biochemistry and Cell Biology, Rice University, Houston, TX 77005, USA.
Proc Natl Acad Sci U S A. 2013 Jul 16;110(29):11940-5. doi: 10.1073/pnas.1310468110. Epub 2013 Jul 1.
Genetic screens have been widely applied to uncover genetic mechanisms of movement disorders. However, most screens rely on human observations of qualitative differences. Here we demonstrate the application of an automatic imaging system to conduct a quantitative screen for genes regulating the locomotive behavior in Caenorhabditis elegans. Two hundred twenty-seven neuronal signaling genes with viable homozygous mutants were selected for this study. We tracked and recorded each animal for 4 min and analyzed over 4,400 animals of 239 genotypes to obtain a quantitative, 10-parameter behavioral profile for each genotype. We discovered 87 genes whose inactivation causes movement defects, including 50 genes that had never been associated with locomotive defects. Computational analysis of the high-content behavioral profiles predicted 370 genetic interactions among these genes. Network partition revealed several functional modules regulating locomotive behaviors, including sensory genes that detect environmental conditions, genes that function in multiple types of excitable cells, and genes in the signaling pathway of the G protein Gαq, a protein that is essential for animal life and behavior. We developed quantitative epistasis analysis methods to analyze the locomotive profiles and validated the prediction of the γ isoform of phospholipase C as a component in the Gαq pathway. These results provided a system-level understanding of how neuronal signaling genes coordinate locomotive behaviors. This study also demonstrated the power of quantitative approaches in genetic studies.
遗传筛选已被广泛应用于揭示运动障碍的遗传机制。然而，大多数筛选依赖于人类对定性差异的观察。在这里，我们展示了一种自动成像系统在进行调控秀丽隐杆线虫运动行为的基因定量筛选中的应用。选择了 227 个具有可育纯合突变体的神经元信号基因进行这项研究。我们跟踪并记录了每只动物 4 分钟，并对 239 种基因型的 4400 多只动物进行了分析，以获得每个基因型的 10 个参数的定量行为特征。我们发现了 87 个失活导致运动缺陷的基因，其中包括 50 个从未与运动缺陷相关的基因。对高内涵行为特征的计算分析预测了这些基因之间存在 370 个遗传相互作用。网络分区揭示了几个调节运动行为的功能模块，包括检测环境条件的感觉基因、在多种兴奋细胞中起作用的基因以及 G 蛋白 Gαq 信号通路中的基因，Gαq 蛋白是动物生命和行为所必需的。我们开发了定量上位性分析方法来分析运动特征，并验证了 γ 型磷脂酶 C 作为 Gαq 途径的组成部分的预测。这些结果提供了对神经元信号基因如何协调运动行为的系统理解。这项研究还证明了定量方法在遗传研究中的强大功能。
