Data-driven prediction of colonization outcomes for complex microbial communities

Lu Wu, Xu-Wen Wang, Zining Tao, Tong Wang, Wenlong Zuo, Yu Zeng, Yang-Yu Liu () and Lei Dai ()
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Lu Wu: Chinese Academy of Sciences
Xu-Wen Wang: Brigham and Women’s Hospital and Harvard Medical School
Zining Tao: Chinese Academy of Sciences
Tong Wang: Brigham and Women’s Hospital and Harvard Medical School
Wenlong Zuo: Chinese Academy of Sciences
Yu Zeng: Chinese Academy of Sciences
Yang-Yu Liu: Brigham and Women’s Hospital and Harvard Medical School
Lei Dai: Chinese Academy of Sciences

Nature Communications, 2024, vol. 15, issue 1, 1-15

Abstract: Abstract Microbial interactions can lead to different colonization outcomes of exogenous species, be they pathogenic or beneficial in nature. Predicting the colonization of exogenous species in complex communities remains a fundamental challenge in microbial ecology, mainly due to our limited knowledge of the diverse mechanisms governing microbial dynamics. Here, we propose a data-driven approach independent of any dynamics model to predict colonization outcomes of exogenous species from the baseline compositions of microbial communities. We systematically validate this approach using synthetic data, finding that machine learning models can predict not only the binary colonization outcome but also the post-invasion steady-state abundance of the invading species. Then we conduct colonization experiments for commensal gut bacteria species Enterococcus faecium and Akkermansia muciniphila in hundreds of human stool-derived in vitro microbial communities, confirming that the data-driven approaches can predict the colonization outcomes in experiments. Furthermore, we find that while most resident species are predicted to have a weak negative impact on the colonization of exogenous species, strongly interacting species could significantly alter the colonization outcomes, e.g., Enterococcus faecalis inhibits the invasion of E. faecium invasion. The presented results suggest that the data-driven approaches are powerful tools to inform the ecology and management of microbial communities.

Date: 2024
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DOI: 10.1038/s41467-024-46766-y

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