Metabolic similarity and the predictability of microbial community assembly

Abstract

When microbial communities form, their composition is shaped by selective pressures imposed by the environment. Can we predict which communities will assemble under different environmental conditions? Here, we hypothesize that quantitative similarities in metabolic traits across metabolically similar environments lead to predictable similarities in community composition. To that end, we measured the growth rate and by-product profile of a library of proteobacterial strains in a large number of single nutrient environments. We found that growth rates and secretion profiles were positively correlated across environments when the supplied substrate was metabolically similar. By analyzing hundreds of in-vitro communities experimentally assembled in an array of different synthetic environments, we then show that metabolically similar substrates select for taxonomically similar communities. These findings lead us to propose and then validate a comparative approach for quantitatively predicting the effects of novel substrates on the composition of complex microbial consortia.

Emergent predictability in microbial ecosystems

“…be linked to the dominant structuring role of pH in these communities. This observation echoes other recent examples where coarse-grained predictions are enabled by general physiological [10], metabolic [3], or toxicity-mediated constraints [35]. Our results indicate that these structuring factors may have higher explanatory power at higher diversity, suggesting the existence of a subtler form of functional convergence, where high diversity leads not to better absolute reproducibility, but to a stronger collapse onto a lower-dimensional subspace governed by metabolic or physiological constraints.…”

“…A long-standing hope of theoretical ecology is that, despite this complexity, some patterns might nevertheless be predictable [1][2][3]. The search for emergent regularities in complex ecosystems is as old as ecology itself.…”

More than the sum of its parts: uncovering emerging effects of microbial interactions in complex communities

“…Furthermore, an additional study demonstrated that shared metabolic traits result in analogous community compositions, particular notable when substrates are metabolically alike. Their approach effectively predicts community composition in novel environments by leveraging insights from communities assembled in environments sharing similar metabolic characteristics (Vila et al 2023 ). While the use of modeling to predict the effects of microbial interactions is to date still in its infancy, the rapid development of AI-based machine learning tools will certainly help to improve community-based metabolic models to predict complex interactions (Asnicar et al 2023 ), and help to assess emerging effects of microbial interactions on larger scales and in different habitats (Jiang et al 2022 ).…”