Journal article
Machine learning aided construction of the quorum sensing communication network for human gut microbiota
- 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.
- Publication status:
- Published
- Peer review status:
- Peer reviewed
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(Preview, Version of record, 2.6MB, Terms of use)
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- Publisher copy:
- 10.1038/s41467-022-30741-6
Authors
- Publisher:
- Springer Nature
- Journal:
- Nature Communications More from this journal
- Volume:
- 13
- Issue:
- 1
- Article number:
- 3079
- Publication date:
- 2022-06-02
- Acceptance date:
- 2022-05-17
- DOI:
- EISSN:
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2041-1723
- Language:
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English
- Keywords:
- Pubs id:
-
1262662
- Local pid:
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pubs:1262662
- Deposit date:
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2022-06-09
Terms of use
- Copyright holder:
- Wu et al
- Copyright date:
- 2022
- Rights statement:
- © The Author(s) 2022. Open Access: This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
- Licence:
- CC Attribution (CC BY)
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