Multi-dimensional experimental and computational exploration of metabolism pinpoints complex probiotic interactions

Guido Zampieri, Georgios Efthimiou, Claudio Angione

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Multi-strain probiotics are widely regarded as effective products for improving gut microbiota stability and host health, providing advantages over single-strain probiotics. However, in general, it is unclear to what extent different strains would cooperate or compete for resources, and how the establishment of a common biofilm microenvironment could influence their interactions. In this work, we develop an integrative experimental and computational approach to comprehensively assess the metabolic functionality and interactions of probiotics across growth conditions. Our approach combines co-culture assays with genome-scale modelling of metabolism and multivariate data analysis, thus exploiting complementary data- and knowledge-driven systems biology techniques. To show the advantages of the proposed approach, we apply it to the study of the interactions between two widely used probiotic strains of Lactobacillus reuteri and Saccharomyces boulardii, characterising their production potential for compounds that can be beneficial to human health. Our results show that these strains can establish a mixed cooperative-antagonistic interaction best explained by competition for shared resources, with an increased individual exchange but an often decreased net production of amino acids and short-chain fatty acids. Overall, our work provides a strategy that can be used to explore microbial metabolic fingerprints of biotechnological interest, capable of capturing multifaceted equilibria even in simple microbial consortia.

Original languageEnglish
Pages (from-to)120-132
Number of pages13
JournalMetabolic Engineering
Early online date28 Jan 2023
Publication statusPublished - 1 Mar 2023

Bibliographical note

Funding Information:
This work was funded by UKRI Research England's THYME project. CA would also like to acknowledge a Network Development Award from The Alan Turing Institute , grant number TNDC2-100022 .

Publisher Copyright:
© 2023 The Authors


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