Machine learning aided construction of the quorum sensing communication network for human gut microbiota

Shengbo Wu, Jie Feng, Chunjiang Liu, Hao Wu, Zekai Qiu, Jianjun Ge, Shuyang Sun, Xia Hong, Yukun Li, Xiaona Wang, Aidong Yang (), Fei Guo () and Jianjun Qiao ()
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Shengbo Wu: Tianjin University
Jie Feng: Tianjin University
Chunjiang Liu: Tianjin University
Hao Wu: Zhejiang Shaoxing Research Institute of Tianjin University
Zekai Qiu: Tianjin University
Jianjun Ge: Tianjin University
Shuyang Sun: Tianjin University
Xia Hong: Tianjin University
Yukun Li: Tianjin University
Xiaona Wang: Tianjin University
Aidong Yang: University of Oxford
Fei Guo: Central South University
Jianjun Qiao: Tianjin University

Nature Communications, 2022, vol. 13, issue 1, 1-13

Abstract: Abstract Quorum sensing (QS) is a cell-cell communication mechanism that connects members in various microbial systems. Conventionally, a small number of QS entries are collected for specific microbes, which is far from being able to fully depict communication-based complex microbial interactions in human gut microbiota. In this study, we propose a systematic workflow including three modules and the use of machine learning-based classifiers to collect, expand, and mine the QS-related entries. Furthermore, we develop the Quorum Sensing of Human Gut Microbes (QSHGM) database ( http://www.qshgm.lbci.net/ ) including 28,567 redundancy removal entries, to bridge the gap between QS repositories and human gut microbiota. With the help of QSHGM, various communication-based microbial interactions can be searched and a QS communication network (QSCN) is further constructed and analysed for 818 human gut microbes. This work contributes to the establishment of the QSCN which may form one of the key knowledge maps of the human gut microbiota, supporting future applications such as new manipulations to synthetic microbiota and potential therapies to gut diseases.

Date: 2022
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DOI: 10.1038/s41467-022-30741-6

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