Division of System Cohort, Medical Institute of Bioregulation, Kyushu University, 3-1-1 Maidashi, Higashi-ku, Fukuoka, Fukuoka 812-8582, Japan.
School of Life Science and Technology, Tokyo Institute of Technology, 2-12-1 Ookayama, Meguro-ku, Tokyo, 152-8550, Japan.
Sci Rep. 2017 Jan 10;7:40164. doi: 10.1038/srep40164.
The identification of the modes of action of bioactive compounds is a major challenge in chemical systems biology of diseases. Genome-wide expression profiling of transcriptional responses to compound treatment for human cell lines is a promising unbiased approach for the mode-of-action analysis. Here we developed a novel approach to elucidate the modes of action of bioactive compounds in a cell-specific manner using large-scale chemically-induced transcriptome data acquired from the Library of Integrated Network-based Cellular Signatures (LINCS), and analyzed 16,268 compounds and 68 human cell lines. First, we performed pathway enrichment analyses of regulated genes to reveal active pathways among 163 biological pathways. Next, we explored potential target proteins (including primary targets and off-targets) with cell-specific transcriptional similarity using chemical-protein interactome. Finally, we predicted new therapeutic indications for 461 diseases based on the target proteins. We showed the usefulness of the proposed approach in terms of prediction coverage, interpretation, and large-scale applicability, and validated the new prediction results experimentally by an in vitro cellular assay. The approach has a high potential for advancing drug discovery and repositioning.
生物活性化合物作用模式的鉴定是疾病化学系统生物学的主要挑战。对人类细胞系中化合物处理的转录反应进行全基因组表达谱分析，是一种用于作用模式分析的很有前途的无偏方法。在这里，我们开发了一种新方法，使用从基于网络的细胞特征综合文库（LINCS）获得的大规模化学诱导转录组数据，以细胞特异性方式阐明生物活性化合物的作用模式，并分析了 16268 种化合物和 68 种人类细胞系。首先，我们对调控基因进行了途径富集分析，以揭示 163 种生物学途径中的活性途径。接下来，我们使用化学-蛋白质相互作用组探索了具有细胞特异性转录相似性的潜在靶蛋白（包括主要靶标和脱靶标）。最后，我们基于靶蛋白预测了 461 种疾病的新治疗适应症。我们从预测覆盖范围、解释和大规模适用性方面展示了所提出方法的有用性，并通过体外细胞测定实验验证了新的预测结果。该方法在推进药物发现和重新定位方面具有很高的潜力。
