Engineering microbial consortia: uptake and leakage ratedifferentially shape community arrangement and composition
Pignon, E.; Hollo, G.; Steiner, T.; van Vliet, S.; Schaerli, Y.
Show abstract
Bacteria often grow as communities in intricate spatial arrangements on surfaces and interact with each other through the local exchange of diffusible molecules. Yet, our understanding of how these interactions shape the properties of the communities remains limited. Here, we study synthetic communities of Escherichia coli amino acid auxotrophs interacting through the obligate exchange of amino acids. We genetically engineer these strains to alter their amino acid leakage and uptake abilities. We then characterise the spatial arrangement and composition of the communities when grown on a surface. By integrating experimental data with mathematical modeling, we demonstrate that amino acid uptake and leakage rates are crucial determinants of community structure. Our results show that while the spatial arrangement of the community is primarily governed by uptake rates, the community composition is predominantly influenced by leakage rates. These findings enhance our understanding of microbial community dynamics and provide a framework for predicting and engineering microbial consortia.
Matching journals
The top 5 journals account for 50% of the predicted probability mass.
Similar papers in this journal
Similar papers in this journal
- Intragenomic conflicts with plasmids and chromosomal mobile genetic elements drive the evolution of natural transformation within species 94%
- Modulation of bacterial multicellularity via spatiotemporal polysaccharide secretion 94%
- Resident microbial communities inhibit growth and antibiotic resistance evolution of Escherichia coli in human gut microbiome samples 94%
Similar papers in this journal
- Nutrient dominance governs the assembly of microbial communities in mixed nutrient environments 96%
- Interactions between metabolism and growth can determine the co-existence of Staphylococcus aureus and Pseudomonas aeruginosa 95%
- Deep Learning Enables Design of Multifunctional Synthetic Human Gut Microbiome Dynamics 95%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.