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

Authors :
Zampieri, Guido
Efthimiou, Georgios
Angione, Claudio
Source :
Metabolic Engineering. Mar2023, Vol. 76, p120-132. 13p.
Publication Year :
2023

Abstract

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. • We propose an approach to predict interactions of probiotics across growth conditions. • Metabolism of L. reuteri and S. boulardii is studied in vitro and in silico. • Multi-strain probiotics can establish a mixed cooperative-antagonistic interaction. • Inter-species interaction are characterised in planktonic and biofilm growth. • In silico models well predict amino acid production across cultures. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
10967176
Volume :
76
Database :
Academic Search Index
Journal :
Metabolic Engineering
Publication Type :
Academic Journal
Accession number :
162288875
Full Text :
https://doi.org/10.1016/j.ymben.2023.01.008