COMMIT: Consideration of metabolite leakage and community composition improves microbial community reconstructions

Abstract

Composition and functions of microbial communities affect important traits in diverse hosts, from crops to humans. Yet, mechanistic understanding of how metabolism of individual microbes is affected by the community composition and metabolite leakage is lacking. Here, we first show that the consensus of automatically generated metabolic reconstructions improves the quality of the draft reconstructions, measured by comparison to reference models. We then devise an approach for gap filling, termed COMMIT, that considers metabolites for secretion based on their permeability and the composition of the community. By applying COMMIT with two soil communities from the Arabidopsis thaliana culture collection, we could significantly reduce the gap-filling solution in comparison to filling gaps in individual reconstructions without affecting the genomic support. Inspection of the metabolic interactions in the soil communities allows us to identify microbes with community roles of helpers and beneficiaries. Therefore, COMMIT offers a versatile fully automated solution for large-scale modelling of microbial communities for diverse biotechnological applications.

Comparative analysis of metabolic models of microbial communities reconstructed from automated tools and consensus approaches

“…(16), gapseq (17) and KBase (18), to generate draft models. Draft models originating from the same MAG were merged to construct draft consensus models by using a recently proposed pipeline (11). Gap-filling of the draft community models was performed using COMMIT (11) (see Methods).…”

“…Constraint-based modeling using GEMs has been used to investigate the activity of different reactions in a metabolic network, including exchange reactions that model interactions between microbes. Numerous studies have employed GEMs to investigate metabolic interactions and functionality within microbial communities, including those found in the human gut (8), termite gut (9), mangrove sediments (10), soil microbial communities (11), and plant root (12). Community-scale metabolic models are typically constructed using: (i) the mixed-bag approach, which involves integrating all metabolic pathways and transport reactions into a single model with one cytosolic and one extracellular compartment; (ii) compartmentalization, where multiple GEMs are combined into a single stoichiometric matrix, with each species assigned to a distinct compartment; (iii) costless secretion, wherein models are simulated using a dynamically and iteratively updated medium based on exchange reactions and metabolites within the community (13, 14).…”

“…During reconstruction, the inclusion of specific reactions in the model depends on the genomic evidence and the network context, often omitting certain reactions based on the modeling objectives. Furthermore, the use of different namespaces for metabolites and reactions from various data sources can pose challenges when combining GEMs (11, 23), leading to further difficulties in predicting metabolic phenotypes of microbial communities.…”

“…Consensus models, formed by integrating different reconstructed models of single species from various tools, have the potential to reduce the uncertainty existed in a single model (24, 25) and can be used to estimate interactions in a community (11). However, a systematic comparison between consensus models and original models in terms of model structure (i.e.…”

“…the number of reactions and metabolites), the inclusion of genes in the model, model functionality, and the potential exchange of metabolites at the community scale is currently lacking. Here, we conducted a comprehensive analysis of these features for models reconstructed using three automated tools, namely: CarveMe, gapseq, and KBase, and a recently proposed consensus reconstruction proposed (11). Our findings shed light on the advantages and limitations of each approach.…”