开放MoA：揭示基于网络拓扑和层次结构的作用机制（MoA）,Bioinformatics

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开放MoA：揭示基于网络拓扑和层次结构的作用机制（MoA）
Bioinformatics ( IF 4.4 ) Pub Date : 2023-11-06 , DOI: 10.1093/bioinformatics/btad666
Xinmeng Liao ₁ , Mehmet Ozcan _{1,

2} , Mengnan Shi ₁ , Woonghee Kim ₁ , Han Jin ₁ , Xiangyu Li ₃ , Hasan Turkez ₄ , Adnane Achour ₅ , Mathias Uhlén ₁ , Adil Mardinoglu _{1,

6} , Cheng Zhang ₁

Affiliation

动机由于组学数据和计算生物学工具的可用性不断增加，系统生物学中的许多方法已应用于药物重新定位。使用包含各种生物相互作用信息的多组学集成网络可以为药物作用机制（MoA）提供更全面的检查和解释。结果我们开发了一个计算管道来剖析药物的隐藏 MoA（开放 MoA）。我们的管道计算代表集成网络中基因/蛋白质之间连接的边缘的置信度得分。显示最高置信度得分的相互作用可以表明潜在的药物靶点并推断潜在的分子 MoAs。Open MoA 还通过测试一些成熟的目标进行了验证。此外，我们应用 Open MoA 揭示了调节 HepG2 细胞中 PKLR 表达的重新定位药物 (JNK-IN-5A) 的 MoA，并发现 STAT1 是关键转录因子。总体而言，Open MoA 代表了第一代工具，可用于预测重新利用药物的潜在 MoA 并剖析从头目标以开发有效的治疗方法。可用性源代码可在 https://github.com/XinmengLiao/Open_MoA 获取。补充信息补充数据可在生物信息学在线获取。

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Open MoA: Revealing the Mechanism of Action (MoA) based on Network Topology and Hierarchy

Motivation Many approaches in systems biology have been applied in drug repositioning due to the increased availability of the omics data and computational biology tools. Using a multi-omics integrated network which contains information of various biological interactions could offer a more comprehensive inspective and interpretation for the drug mechanism of action (MoA). Results We developed a computational pipeline for dissecting the hidden MoAs of drugs (Open MoA). Our pipeline computes confidence scores to edges that represent connections between genes/proteins in the integrated network. The interactions showing the highest confidence score could indicate potential drug targets and infer the underlying molecular MoAs. Open MoA was also validated by testing some well-established targets. Additionally, we applied Open MoA to reveal the MoA of a repositioned drug (JNK-IN-5A) that modulates the PKLR expression in HepG2 cells and found STAT1 is the key transcription factor. Overall, Open MoA represents a first-generation tool that could be utilized for predicting the potential MoA of repurposed drugs and dissecting de novo targets for developing effective treatments. Availability Source code is available at https://github.com/XinmengLiao/Open_MoA. Supplementary information Supplementary data are available at Bioinformatics online.

更新日期：2023-11-06

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