Project description:The ecological forces that govern the assembly and stability of the human gut microbiota remain unresolved. We developed a generalizable model-guided framework to predict higher-dimensional consortia from time-resolved measurements of lower-order assemblages. This method was employed to decipher microbial interactions in a diverse human gut microbiome synthetic community. We show that pairwise interactions are major drivers of multi-species community dynamics, as opposed to higher-order interactions. The inferred ecological network exhibits a high proportion of negative and frequent positive interactions. Ecological drivers and responsive recipient species were discovered in the network. Our model demonstrated that a prevalent positive and negative interaction topology enables robust coexistence by implementing a negative feedback loop that balances disparities in monospecies fitness levels. We show that negative interactions could generate history-dependent responses of initial species proportions that frequently do not originate from bistability. Measurements of extracellular metabolites illuminated the metabolic capabilities of monospecies and potential molecular basis of microbial interactions. In sum, these methods defined the ecological roles of major human-associated intestinal species and illuminated design principles of microbial communities.
Project description:In this study, we created a synthetic mucin-degrading microbial community to specifically study mucin-driven ecological interactions in vitro. The synthetic community consisted of primary mucin degraders and cross-feeders. We tracked community composition and dynamics and the mucin-degrading enzymes that were produced.
Project description:Despite that most microorganisms live as part of community, we have modest knowledge about the interactions among microbial community members in nature, and the implications of those interactions for emergent community properties or ecosystem-relevant functions. To facilitate advances in understanding microbial interactions, we describe a straightforward synthetic community system for interrogating the extracellular interactions among microbial community members. The laboratory-scale system physically separates microbial populations within the community, but allows for chemical interactions via a shared media reservoir. Community goods, including small molecules, extracellular enzymes, and antibiotics, can be assayed using sensitive mass spectrometry, and community member outcomes can be assayed, for example, using flow cytometry, biomass measurements, and transcript analyses. The synthetic community design allows for determining the causes and consequences of community diversity and functional outcomes given manipulation of community membership or structure, abiotic stressors, or temporal dynamics. Because it is versatile to accommodate any artificial or environmental microbiome members, scalable to high-throughput capacity, flexible to an array of experimental designs, and accessible to a variety of laboratories because no specialized or costly components are required, this synthetic community system has the potential to practically advance knowledge of microbial interactions within both natural and artificial communities.
Project description:Microbial communities colonize plant tissues and contribute to host function. How these communities form and how individual members contribute to shaping the microbial community are not well understood. Synthetic microbial communities, where defined individual isolates are combined, can serve as valuable model systems for uncovering the organizational principles of communities. Using genome-defined organisms, systematic analysis by computationally-based network reconstruction can lead to mechanistic insights and the metabolic interactions between species. In this study, 10 bacterial strains isolated from the Populus deltoides rhizosphere were combined and passaged in two different media environments to form a stable microbial community. The membership and relative abundances of the strains stabilized after around 5 growth cycles and resulted in just a few dominant strains. To unravel the underlying metabolic interactions, the KBase platform was used for constructing community-level models and for elucidating the metabolic processes involved in shaping the microbial communities. These analyses were complemented by growth curves of the individual isolates, pairwise interaction screens, and metaproteomics of the community. Flux balance analysis was used to model the metabolic potential in the microbial community and identify potential metabolic exchanges among the component species. Revealing the mechanisms of interaction among plant-associated microorganisms will provide insights into strategies for engineering microbial communities that can potentially increase plant growth and disease resistance. Further, deciphering the membership and metabolic potentials of a bacterial community will enable the design of synthetic co-cultures with desired biological functions.