Abstract
The study of microbial communities and their interactions has attracted the interest of the scientific community, because of their potential for applications in biotechnology, ecology and medicine. The complexity of interspecies interactions, which are key for the macroscopic behavior of microbial communities, cannot be studied easily experimentally. For this reason, the modeling of microbial communities has begun to leverage the knowledge of established constraint-based methods, which have long been used for studying and analyzing the microbial metabolism of individual species based on genome-scale metabolic reconstructions of microorganisms. A main problem of genome-scale metabolic reconstructions is that they usually contain metabolic gaps due to genome misannotations and unknown enzyme functions. This problem is traditionally solved by using gap-filling algorithms that add biochemical reactions from external databases to the metabolic reconstruction, in order to restore model growth. However, gap-filling algorithms could evolve by taking into account metabolic interactions among species that coexist in microbial communities. In this work, a gap-filling method that resolves metabolic gaps at the community level was developed. The efficacy of the algorithm was tested by analyzing its ability to resolve metabolic gaps on a synthetic community of auxotrophic Escherichia coli strains. Subsequently, the algorithm was applied to resolve metabolic gaps and predict metabolic interactions in a community of Bifidobacterium adolescentis and Faecalibacterium prausnitzii, two species present in the human gut microbiota, and in an experimentally studied community of Dehalobacter and Bacteroidales species of the ACT-3 community. The community gap-filling method can facilitate the improvement of metabolic models and the identification of metabolic interactions that are difficult to identify experimentally in microbial communities.
Highlights
Microorganisms form the most abundant group of living organisms on our planet
For the generation of the results presented in this paper, the Mixed Integer Linear Programming (MILP) problem was formulated and solved in MATLAB 2017b with CPLEX Interactive Optimizer 12.8 and CobraToolbox 3, and alternative solutions for the problem were calculated with the populate procedure of CPLEX and an upper time limit of two hours
We discuss the best solution calculated by the algorithm, i.e. the solution that adds the minimum number of biochemical reactions to the models, as well as the patterns emerging from the ten best alternative solutions suggested by the algorithm
Summary
Microorganisms form the most abundant group of living organisms on our planet. In nature microorganisms do not live in isolation, but in close association with one another, forming microbial communities. The study of microbiomes has been extensively limited to the taxonomic classification of species and their correlation with different phenotypes. Such correlations do not offer any mechanistic explanation on how the interactions among microbes and their environment form the observed phenotypes of the ecosystem [4]. A way to elucidate metabolic interactions in microbial communities comes from the use of a set of mathematical and computational techniques, called constraint-based methods, that make use of genome-scale metabolic models [5, 6]. Constraint-based methods have been used extensively for the study of the metabolic functions of individual microorganisms as well as for strain design in metabolic engineering [7]. In the context of microbial communities, methods like SteadyCom [8], OptCom [9], d-OptCom [10], DMMM (Dynamic Multispecies Metabolic Modeling) [11], and COMETS (Computation Of Microbial Ecosystems in Time and Space) [12] give the opportunity to evaluate growth rates and metabolic interactions of community members under various conditions
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