Wu Shengbo, Yang Shujuan, Wang Manman, Song Nan, Feng Jie, Wu Hao, Yang Aidong, Liu Chunjiang, Li Yanni, Guo Fei, Qiao Jianjun
School of Chemical Engineering and Technology, Tianjin University, Tianjin, 300072, China.
State Key Laboratory of Chemical Engineering, Tianjin University, Tianjin, 300072, China.
Sci China Life Sci. 2023 Jan;66(1):137-151. doi: 10.1007/s11427-021-2121-0. Epub 2022 Aug 4.
Many diseases and health conditions are closely related to various microbes, which participate in complex interactions with diverse drugs; nonetheless, the detailed targets of such drugs remain to be elucidated. Many existing studies have reported causal associations among drugs, gut microbes, or diseases, calling for a workflow to reveal their intricate interactions. In this study, we developed a systematic workflow comprising three modules to construct a Quorum Sensing-based Drug-Microbe-Disease (QS-DMD) database ( http://www.qsdmd.lbci.net/ ), which includes diverse interactions for more than 8,000 drugs, 163 microbes, and 42 common diseases. Potential interactions between microbes and more than 8,000 drugs have been systematically studied by targeting microbial QS receptors combined with a docking-based virtual screening technique and in vitro experimental validations. Furthermore, we have constructed a QS-based drug-receptor interaction network, proposed a systematic framework including various drug-receptor-microbe-disease connections, and mapped a paradigmatic circular interaction network based on the QS-DMD, which can provide the underlying QS-based mechanisms for the reported causal associations. The QS-DMD will promote an understanding of personalized medicine and the development of potential therapies for diverse diseases. This work contributes to a paradigm for the construction of a molecule-receptor-microbe-disease interaction network for human health that may form one of the key knowledge maps of precision medicine in the future.
许多疾病和健康状况与各种微生物密切相关，这些微生物与多种药物参与复杂的相互作用；然而，此类药物的详细靶点仍有待阐明。许多现有研究报告了药物、肠道微生物或疾病之间的因果关联，这就需要一种工作流程来揭示它们之间复杂的相互作用。在本研究中，我们开发了一个由三个模块组成的系统工作流程，以构建一个基于群体感应的药物-微生物-疾病（QS-DMD）数据库（http://www.qsdmd.lbci.net/），该数据库包含8000多种药物、163种微生物和42种常见疾病的多种相互作用。通过靶向微生物群体感应受体，结合基于对接的虚拟筛选技术和体外实验验证，系统地研究了微生物与8000多种药物之间的潜在相互作用。此外，我们构建了基于群体感应的药物-受体相互作用网络，提出了一个包括各种药物-受体-微生物-疾病联系的系统框架，并绘制了基于QS-DMD的典型循环相互作用网络，这可以为所报告的因果关联提供潜在的基于群体感应的机制。QS-DMD将促进对个性化医疗的理解以及针对多种疾病的潜在治疗方法的开发。这项工作为构建人类健康的分子-受体-微生物-疾病相互作用网络提供了一种范例，该网络可能会成为未来精准医学的关键知识图谱之一。
