Department of Cell Biology, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA.
Graduate Program for Nanomedical Science, Yonsei University, Seoul, Korea.
Cell Chem Biol. 2019 Oct 17;26(10):1380-1392.e6. doi: 10.1016/j.chembiol.2019.07.008. Epub 2019 Aug 1.
Gene expression signature-based inference of functional connectivity within and between genetic perturbations, chemical perturbations, and disease status can lead to the development of actionable hypotheses for gene function, chemical modes of action, and disease treatment strategies. Here, we report a FuSiOn-based genome-wide integration of hypomorphic cellular phenotypes that enables functional annotation of gene network topology, assignment of mechanistic hypotheses to genes of unknown function, and detection of cooperativity among cell regulatory systems. Dovetailing genetic perturbation data with chemical perturbation phenotypes allowed simultaneous generation of mechanism of action hypotheses for thousands of uncharacterized natural products fractions (NPFs). The predicted mechanism of actions span a broad spectrum of cellular mechanisms, many of which are not currently recognized as "druggable." To enable use of FuSiOn as a hypothesis generation resource, all associations and analyses are available within an open source web-based GUI (http://fusion.yuhs.ac).
基于基因表达谱的遗传扰动、化学扰动和疾病状态的功能连接推断,可以为基因功能、化学作用模式和疾病治疗策略的可行假设的发展提供依据。在这里,我们报告了基于 FuSiOn 的全基因组低细胞表型的整合,这使得基因网络拓扑的功能注释、对未知功能基因的机制假设的分配以及细胞调控系统之间的协同作用的检测成为可能。将遗传扰动数据与化学扰动表型相结合,允许同时为数千个未表征的天然产物分数 (NPF) 生成作用机制假设。预测的作用机制涵盖了广泛的细胞机制,其中许多目前还不被认为是“可用药”的。为了使 FuSiOn 能够作为假设生成资源使用,所有的关联和分析都可以在一个开源的基于网络的 GUI 中使用(http://fusion.yuhs.ac)。