Michael Silverstein, Jennifer Bhatnagar, and Daniel Segrè
Microbial communities are shaped by environmental metabolites, but the principles that govern whether different communities will converge or diverge in any given condition remain unknown, posing fundamental questions about the feasibility of microbiome engineering. Here we studied the longitudinal assembly dynamics of a set of natural microbial communities grown in laboratory conditions of increasing metabolic complexity. We found that different microbial communities tend to become similar to each other when grown in metabolically simple conditions, but they diverge in composition as the metabolic complexity of the environment increases, a phenomenon we refer to as the divergence-complexity effect. A comparative analysis of these communities revealed that this divergence is driven by community diversity and by the assortment of specialist taxa capable of degrading complex metabolites. An ecological model of community dynamics indicates that the hierarchical structure of metabolism itself, where complex molecules are enzymatically degraded into progressively simpler ones that then participate in cross-feeding between community members, is necessary and sufficient to recapitulate our experimental observations. In addition to helping understand the role of the environment in community assembly, the divergence-complexity effect can provide insight into which environments support multiple community states, enabling the search for desired ecosystem functions towards microbiome engineering applications.
All raw 16S sequencing data for this study can be accessed with the NCBI BioProject accession PRJNA1074799 (https://www.ncbi.nlm.nih.gov/sra/PRJNA1074799).
All 16S samples were processed into ASV tables using a QIIME2 pipeline at Boston University: https://github.com/Boston-University-Microbiome-Initiative/BU16s.
All processed ASV tables and associated metadata files used in the manuscript are provided in this repository:
| Study | ASV Table | Metadata Table |
|---|---|---|
| Meta-analysis | ASV | Metadata |
| Our's | ASV | Metadata |
Meta-analysis was conducted by re-analyzing data from Goldford et al. 2018 (https://doi.org/10.1126/science.aat1168) and Bittleston et al. 2020 (https://doi.org/10.1038/s41467-020-15169-0).
Code for consumer-resource model simulations is available in env_complexity_simulation_v2.ipynb. Functions for generating trophic c and D matrices can be found at https://github.com/michaelsilverstein/ms_tools/blob/main/ms_tools/crm.py.
All data analysis and figures were generated in env_complexity_publication_figures.ipynb.
Silverstein, M.R., Bhatnagar, J.M. & Segrè, D. Metabolic complexity drives divergence in microbial communities. Nat Ecol Evol (2024). https://doi.org/10.1038/s41559-024-02440-6