Design of synthetic human gut microbiome assembly and butyrate production

Abstract

The capability to design microbiomes with predictable functions would enable new technologies for applications in health, agriculture, and bioprocessing. Towards this goal, we develop a model-guided approach to design synthetic human gut microbiomes for production of the health-relevant metabolite butyrate. Our data-driven model quantifies microbial interactions impacting growth and butyrate production separately, providing key insights into ecological mechanisms driving butyrate production. We use our model to explore a vast community design space using a design-test-learn cycle to identify high butyrate-producing communities. Our model can accurately predict community assembly and butyrate production across a wide range of species richness. Guided by the model, we identify constraints on butyrate production by high species richness and key molecular factors driving butyrate production, including hydrogen sulfide, environmental pH, and resource competition. In sum, our model-guided approach provides a flexible and generalizable framework for understanding and accurately predicting community assembly and metabolic functions.

Therapeutic Potential of Butyrate for Treatment of Type 2 Diabetes

“…However, administration of E. hallii strain L2-7 (now reclassified as Anaerobutyricum soehngenii ) to individuals with insulin resistance improved insulin sensitivity only in individuals with a specific gut microbiota at baseline ( 98 ), reflecting both the resilience of the human gut microbiota and the ecological interactions of commensal microbes in the communities that might be species-specific. To produce effective NGPs, advanced data-driven metagenomics approaches ( 99 ) and specific isolation efforts might be required to develop synthetic microbial communities targeted to produce butyrate.…”

An evolutionary algorithm for designing microbial communities via environmental modification

“…The rational design of multispecies communities toward defined phenotypes is an enticing, yet challenging, goal of synthetic ecology. As the phenotypic traits of microbiomes in complex settings remain difficult to predict [29,76,77], fulfilling this potential will require a synthesis of computational and experimental methods that focus on different aspects of these communities [10,[78][79][80]. Here, we used in silico microbial communities to show how their ecological properties can be modulated via environmental modification and presented a search algorithm to identify specific nutrient combinations that would result in desired phenotypes.…”

Modeling approaches for probing cross-feeding interactions in the human gut microbiome

“…At least 30,000 interactions could be counted at a specific time in the gut microbiota [8] . Conversely, current experimental set-ups to study microbial interactions have analyzed only small synthetic consortia up to 25 gut species [9] , [10] . There is evidently a gap in our understanding of microbial interactions, which has raised the interest in top-down systems biology approaches to simplify this tremendous complexity.…”

“…While cross-feeding interactions are at the core of the gut microbiome network [8] , [9] , [23] , there is a lack of quantitative studies revealing their actual prevalence in the gut. Among other approaches, kinetic models based on ordinary differential equations (ODE) have been used to quantify the extent of interactions and influence of certain gut microbes, among others [24] .…”