National Engineering Research Center for Miniaturized Detection Systems, College of Life Sciences, Northwest University, Xi'an, P.R. China.
Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada.
Sci Rep. 2019 Sep 25;9(1):13868. doi: 10.1038/s41598-019-50224-x.
Identification of primary targets associated with phenotypes can facilitate exploration of the underlying molecular mechanisms of compounds and optimization of the structures of promising drugs. However, the literature reports limited effort to identify the target major isoform of a single known target gene. The majority of genes generate multiple transcripts that are translated into proteins that may carry out distinct and even opposing biological functions through alternative splicing. In addition, isoform expression is dynamic and varies depending on the developmental stage and cell type. To identify target major isoforms, we integrated a breast cancer type-specific isoform coexpression network with gene perturbation signatures in the MCF7 cell line in the Connectivity Map database using the 'shortest path' drug target prioritization method. We used a leukemia cancer network and differential expression data for drugs in the HL-60 cell line to test the robustness of the detection algorithm for target major isoforms. We further analyzed the properties of target major isoforms for each multi-isoform gene using pharmacogenomic datasets, proteomic data and the principal isoforms defined by the APPRIS and STRING datasets. Then, we tested our predictions for the most promising target major protein isoforms of DNMT1, MGEA5 and P4HB4 based on expression data and topological features in the coexpression network. Interestingly, these isoforms are not annotated as principal isoforms in APPRIS. Lastly, we tested the affinity of the target major isoform of MGEA5 for streptozocin through in silico docking. Our findings will pave the way for more effective and targeted therapies via studies of drug targets at the isoform level.
鉴定与表型相关的主要靶标可以促进对化合物潜在分子机制的探索，并优化有前途药物的结构。然而，文献报道在鉴定单个已知靶标基因的主要靶标同工型方面所做的努力有限。大多数基因产生多个转录本，这些转录本翻译成的蛋白质可能通过选择性剪接执行不同的甚至相反的生物学功能。此外，同工型表达是动态的，并且根据发育阶段和细胞类型而变化。为了鉴定主要靶标同工型，我们整合了乳腺癌特异性同工型共表达网络与 Connectivity Map 数据库中 MCF7 细胞系中的基因扰动特征，使用“最短路径”药物靶标优先级方法。我们使用白血病癌症网络和 HL-60 细胞系中药物的差异表达数据来测试用于检测主要靶标同工型的算法的稳健性。我们进一步使用药物基因组数据集、蛋白质组数据和 APPRIS 和 STRING 数据集定义的主要同工型分析每个多同工型基因的主要靶标同工型的特性。然后，我们根据共表达网络中的表达数据和拓扑特征测试了我们对 DNMT1、MGEA5 和 P4HB4 最有前途的靶标主要蛋白同工型的预测。有趣的是，这些同工型在 APPRIS 中未被注释为主导同工型。最后，我们通过计算机对接测试了 MGEA5 的靶标主要同工型与链脲佐菌素的亲和力。我们的研究结果将为通过在同工型水平上研究药物靶标来开展更有效和有针对性的治疗铺平道路。
