Data-driven prediction of colonization outcomes for complex microbial communities
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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.
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- Authors
- Wu, L., Wang, X.-W., Tao, Z., Wang, T., Zuo, W., Zeng, Y., Liu, Y.-Y., & Dai, L.
- Affiliations
- Chinese Academy of Sciences (CN)
- Brigham and Women's Hospital (US)
- Corresponding author
- Liu, Yang-Yu
- Dai, Lei
- Venue
- Nature Communications
- Published
- 2024-03-16
- Volume
- 15
- Issue
- 1
- Pages
- 2406-2406
- ISSN
- 2041-1723 (ISSN-L)
- PMC
- PMC10944475
- OpenAlex
- W4392878377
- Cited by
- 44
- References
- 12
- Citations by year
- 2026: 16 · 2025: 24 · 2024: 3 · 2023: 1
- FWCI
- 5.85
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Nature Communications (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion)
- Research Square (acceptedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- PubMed Central (submittedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Natural Science Foundation of China (31971513)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gut microbiota and health
- Physical Sciences › Environmental Science › Ecology › Microbial Community Ecology and Physiology
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bacterial biofilms and quorum sensing
- Keywords
- microbial colonization, complex microbial communities, data-driven prediction, machine learning, gut microbiota, Enterococcus faecium, Akkermansia muciniphila, Enterococcus faecalis, microbial interactions, commensal bacteria, synthetic microbial communities
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