Deciphering microbial interactions in synthetic human gut microbiome communities
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ABSTRACT:
PROVIDER: PRJEB26607 | ENA |
REPOSITORIES: ENA
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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.
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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.
2017-10-26 | MTBLS525 | MetaboLights
Project description:Deciphering cis-regulatory logic with 100 million synthetic promoters (MNase)
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Project description:Synthetic microbial communities of Arabidopsis thaliana in reconstitution experiments
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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.
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Project description:Microbial communities treating synthetic wastewatrer containg carbamazepine
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Project description:Though most microorganisms live within a community, we have modest knowledge about microbial interactions and their implications for community properties and ecosystem functions. To advance understanding of microbial interactions, we describe a straightforward synthetic community system that can be used to interrogate exometabolite interactions among microorganisms. The filter plate system (also known as the Transwell system) physically separates microbial populations, but allows for chemical interactions via a shared medium reservoir. Exometabolites, including small molecules, extracellular enzymes, and antibiotics, are assayed from the reservoir using sensitive mass spectrometry. Community member outcomes, such as growth, productivity, and gene regulation, can be determined using flow cytometry, biomass measurements, and transcript analyses, respectively. The synthetic community design allows for determination of the consequences of microbiome diversity for emergent community properties and for functional changes over time or after perturbation. Because it is versatile, scalable, and accessible, this synthetic community system has the potential to practically advance knowledge of microbial interactions that occur within both natural and artificial communities. See publications: https://journals.asm.org/doi/10.1128/mSystems.00129-17 and https://journals.asm.org/doi/10.1128/mSystems.00493-20 and https://www.biorxiv.org/content/10.1101/2021.09.05.459016v2.full.
The work (proposal:https://doi.org/10.46936/10.25585/60000724) conducted by the U.S. Department of Energy Joint Genome Institute (https://ror.org/04xm1d337), a DOE Office of Science User Facility, is supported by the Office of Science of the U.S. Department of Energy operated under Contract No. DE-AC02-05CH11231.
2023-11-02 | MSV000093275 | MassIVE
Project description:Understanding the principles of colonization resistance of the gut microbiome to the pathogen Clostridioides difficile will enable the design of defined bacterial therapeutics. We investigate the ecological principles of community resistance to C. difficile using a synthetic human gut microbiome. Using a dynamic computational model, we demonstrate that C. difficile receives the largest number and magnitude of incoming negative interactions. Our results show that C. difficile is in a unique class of species that display a strong negative dependence between growth and species richness. We identify molecular mechanisms of inhibition including acidification of the environment and competition over resources. We demonstrate that Clostridium hiranonis strongly inhibits C. difficile partially via resource competition. Increasing the initial density of C. difficile can increase its abundance in the assembled community, but community context determines the maximum achievable C. difficile abundance. Our work suggests that the C. difficile inhibitory potential of defined bacterial therapeutics can be optimized by designing communities featuring a combination of mechanisms including species richness, environment acidification, and resource competition.
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