Classifying Interactions in a Synthetic Bacterial Community Is Hindered by Inhibitory Growth Medium.
Détails
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Accès restreint UNIL
Etat: Public
Version: Final published version
Licence: CC BY 4.0
Accès restreint UNIL
Etat: Public
Version: Final published version
Licence: CC BY 4.0
ID Serval
serval:BIB_91D9BD85F2C5
Type
Article: article d'un périodique ou d'un magazine.
Collection
Publications
Institution
Titre
Classifying Interactions in a Synthetic Bacterial Community Is Hindered by Inhibitory Growth Medium.
Périodique
mSystems
ISSN
2379-5077 (Print)
ISSN-L
2379-5077
Statut éditorial
Publié
Date de publication
26/10/2022
Peer-reviewed
Oui
Volume
7
Numéro
5
Pages
e0023922
Langue
anglais
Notes
Publication types: Journal Article ; Research Support, Non-U.S. Gov't
Publication Status: ppublish
Publication Status: ppublish
Résumé
Predicting the fate of a microbial community and its member species relies on understanding the nature of their interactions. However, designing simple assays that distinguish between interaction types can be challenging. Here, we performed spent medium assays based on the predictions of a mathematical model to decipher the interactions among four bacterial species: Agrobacterium tumefaciens, Comamonas testosteroni, Microbacterium saperdae, and Ochrobactrum anthropi. While most experimental results matched model predictions, the behavior of C. testosteroni did not: its lag phase was reduced in the pure spent media of A. tumefaciens and M. saperdae but prolonged again when we replenished our growth medium. Further experiments showed that the growth medium actually delayed the growth of C. testosteroni, leading us to suspect that A. tumefaciens and M. saperdae could alleviate this inhibitory effect. There was, however, no evidence supporting such "cross-detoxification," and instead, we identified metabolites secreted by A. tumefaciens and M. saperdae that were then consumed or "cross-fed" by C. testosteroni, shortening its lag phase. Our results highlight that even simple, defined growth media can have inhibitory effects on some species and that such negative effects need to be included in our models. Based on this, we present new guidelines to correctly distinguish between different interaction types such as cross-detoxification and cross-feeding. IMPORTANCE Communities of microbes colonize virtually every place on earth. Ultimately, we strive to predict and control how these communities behave, for example, if they reside in our guts and make us sick. But precise control is impossible unless we can identify exactly how their member species interact with one another. To find a systematic way to measure interactions, we started very simply with a small community of four bacterial species and carefully designed experiments based on a mathematical model. This first attempt accurately mapped out interactions for all species except one. By digging deeper, we understood that our method failed for that species as it was suffering in the growth medium that we chose. A revised model that considered that growth media can be harmful could then make more accurate predictions. What we have learned with these four species can now be applied to decipher interactions in larger communities.
Mots-clé
Bacteria/metabolism, Comamonas testosteroni, Models, Theoretical, Microbiota, Actinomycetales, consumer-resource model, cross-feeding, detoxification, experimental design, spent media
Pubmed
Web of science
Open Access
Oui
Création de la notice
10/10/2022 13:10
Dernière modification de la notice
09/03/2023 6:49