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基于群体感应的药物、微生物和疾病之间的相互作用。

Quorum sensing-based interactions among drugs, microbes, and diseases.

作者信息

Wu Shengbo, Yang Shujuan, Wang Manman, Song Nan, Feng Jie, Wu Hao, Yang Aidong, Liu Chunjiang, Li Yanni, Guo Fei, Qiao Jianjun

机构信息

School of Chemical Engineering and Technology, Tianjin University, Tianjin, 300072, China.

State Key Laboratory of Chemical Engineering, Tianjin University, Tianjin, 300072, China.

出版信息

Sci China Life Sci. 2023 Jan;66(1):137-151. doi: 10.1007/s11427-021-2121-0. Epub 2022 Aug 4.

Abstract

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.

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