Metabolic interactions affect the biomass of synthetic bacterial biofilm communities

Xinli Sun, Jiyu Xie, Daoyue Zheng, Riyan Xia, Wei Wang, Weibing Xun, Qiwei Huang, Ruifu Zhang, Ákos T. Kovács, Zhihui Xu*, Qirong Shen

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

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Abstract

ABSTRACT Microbes typically reside in communities containing multiple species, whose interactions have considerable impacts on the robustness and functionality of such communities. To manage microbial communities, it is essential to understand the factors driving their assemblage and maintenance. Even though the community composition could be easily assessed, interspecies interactions during community establishment remain poorly understood. Here, we combined co-occurrence network analysis with quantitative PCR to examine the importance of each species within synthetic communities (SynComs) of pellicle biofilms. Genome-scale metabolic models and in vitro experiments indicated that the biomass of SynComs was primarily affected by keystone species that are acting either as metabolic facilitators or as competitors. Our study sets an example of how to construct a SynCom and investigate interspecies interactions.

IMPORTANCE Co-occurrence network analysis is an effective tool for predicting complex networks of microbial interactions in the natural environment. Using isolates from a rhizosphere, we constructed multi-species biofilm communities and investigated co-occurrence patterns between microbial species in genome-scale metabolic models and in vitro experiments. According to our results, metabolic exchanges and resource competition may partially explain the co-occurrence network analysis results found in synthetic bacterial biofilm communities.
Original languageEnglish
Article numbere0104523
JournalmSystems
Volume8
Issue number6
Number of pages17
ISSN2379-5077
DOIs
Publication statusPublished - 2023

Keywords

  • Soil microbiology
  • SynComs
  • Biofilms
  • Network analysis
  • Metabolic modeling

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