Evidence for a multi-level trophic organization of the human gut microbiome

Abstract

The human gut microbiome is a complex ecosystem, in which hundreds of microbial species and metabolites coexist, in part due to an extensive network of cross-feeding interactions. However, both the large-scale trophic organization of this ecosystem, and its effects on the underlying metabolic flow, remain unexplored. Here, using a simplified model, we provide quantitative support for a multi-level trophic organization of the human gut microbiome, where microbes consume and secrete metabolites in multiple iterative steps. Using a manually-curated set of metabolic interactions between microbes, our model suggests about four trophic levels, each characterized by a high level-to-level metabolic transfer of byproducts. It also quantitatively predicts the typical metabolic environment of the gut (fecal metabolome) in approximate agreement with the real data. To understand the consequences of this trophic organization, we quantify the metabolic flow and biomass distribution, and explore patterns of microbial and metabolic diversity in different levels. The hierarchical trophic organization suggested by our model can help mechanistically establish causal links between the abundances of microbes and metabolites in the human gut.

Ecology-guided prediction of cross-feeding interactions in the human gut microbiome

“…Our approach uses the idea that we can leverage cross-feeding interactions -which comprise knowing the metabolites that each microbial species is capable of consuming and producing -to mechanistically connect the levels of microbes and metabolites in the human gut. Several different mechanistic models in past studies have shown that this is indeed possible 18,20,29,35,36 . While GutCP is generalizable and can be used with any of these models, in this manuscript, we use a previously published consumer-resource model 20 .…”

“…GutCP combines machine learning techniques 33,34 with an ecological model of the microbiome. The ecological model is effective at bootstrapping previously-known direct interactions and estimating the metabolic environment of the gut in agreement with experimental measurements 20 . GutCP uses these estimates as a leverage to predict new cross-feeding interactions.…”

“…A promising framework to realize such an understanding is a fully mechanistic model of the microbiome [17][18][19][20] , which can connect the levels of microbial species and metabolites with each other quantitatively. An essential first step in building this model is establishing which metabolic interactions are relevant in the human gut microbiome 18,[20][21][22] .…”

“…A promising framework to realize such an understanding is a fully mechanistic model of the microbiome [17][18][19][20] , which can connect the levels of microbial species and metabolites with each other quantitatively. An essential first step in building this model is establishing which metabolic interactions are relevant in the human gut microbiome 18,[20][21][22] . Indeed, inferring crossfeeding interactions is an active and important field of microbiome research, and employs both direct 9,[23][24][25] and indirect 11,[26][27][28][29] inference methods.…”

Metabolite-mediated modelling of microbial community dynamics captures emergent behaviour more effectively than species–species modelling

“…Universal building block interactions could be used to build purpose-built predictive dynamic models in an "n of one" manner, meaning built with data from a single patient [9,10]. It is as yet an open question as to the nature of these building blocks, and indeed if any can be found [11,12,13,14,15]. Here, we examine two popular modeling frameworks, species-species interaction (SSI) and species-metabolite interaction (SMI) models, and evaluate them using interdependent growth experiments of single species, pairs, and trios from Friedman et al [16].…”