Environments that Induce Synthetic Microbial Ecosystems

Interactions between microbial species are sometimes mediated by the exchange of small molecules, secreted by one species and metabolized by another. Both one-way (commensal) and two-way (mutualistic) interactions may contribute to complex networks of interdependencies. Understanding these interacti...

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Veröffentlicht in:PLoS computational biology Jg. 6; H. 11; S. e1001002
Hauptverfasser: Klitgord, Niels, Segrè, Daniel
Format: Journal Article
Sprache:Englisch
Veröffentlicht: United States Public Library of Science 18.11.2010
Public Library of Science (PLoS)
Schlagworte:
Algorithms
Analysis
Astrobiology
Biochemistry/Bioinformatics
Biochemistry/Theory and Simulation
Biotechnology/Applied Microbiology
Computational Biology/Ecosystem Modeling
Computational Biology/Metabolic Networks
Computational Biology/Synthetic Biology
Computational Biology/Systems Biology
Culture Media - chemistry
Culture Media - metabolism
Desulfovibrio vulgaris - metabolism
E coli
Ecology
Ecology/Theoretical Ecology
Ecosystem
Ecosystems
Environmental sustainability
Escherichia coli - metabolism
Evolutionary Biology/Evolutionary Ecology
Experiments
Genetic algorithms
Genetic aspects
Genetics and Genomics/Microbial Evolution and Genomics
Metabolites
Methanococcus - metabolism
Microbial Interactions
Microbial metabolism
Microbiology
Microbiology/Environmental Microbiology
Microbiology/Microbial Physiology and Metabolism
Models, Biological
Physiological aspects
Public Health and Epidemiology/Infectious Diseases
Saccharomyces cerevisiae - metabolism
Studies
Synthetic Biology - methods
Systems Biology - methods
United States
Synthetic Biology
Algorithms
Methanococcus
Culture Media
Models, Biological
Ecosystem
Escherichia coli
Desulfovibrio vulgaris
Microbial Interactions
Saccharomyces cerevisiae
Systems Biology
ISSN:1553-7358, 1553-734X, 1553-7358
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Beschreibung
Zusammenfassung:Interactions between microbial species are sometimes mediated by the exchange of small molecules, secreted by one species and metabolized by another. Both one-way (commensal) and two-way (mutualistic) interactions may contribute to complex networks of interdependencies. Understanding these interactions constitutes an open challenge in microbial ecology, with applications ranging from the human microbiome to environmental sustainability. In parallel to natural communities, it is possible to explore interactions in artificial microbial ecosystems, e.g. pairs of genetically engineered mutualistic strains. Here we computationally generate artificial microbial ecosystems without re-engineering the microbes themselves, but rather by predicting their growth on appropriately designed media. We use genome-scale stoichiometric models of metabolism to identify media that can sustain growth for a pair of species, but fail to do so for one or both individual species, thereby inducing putative symbiotic interactions. We first tested our approach on two previously studied mutualistic pairs, and on a pair of highly curated model organisms, showing that our algorithms successfully recapitulate known interactions, robustly predict new ones, and provide novel insight on exchanged molecules. We then applied our method to all possible pairs of seven microbial species, and found that it is always possible to identify putative media that induce commensalism or mutualism. Our analysis also suggests that symbiotic interactions may arise more readily through environmental fluctuations than genetic modifications. We envision that our approach will help generate microbe-microbe interaction maps useful for understanding microbial consortia dynamics and evolution, and for exploring the full potential of natural metabolic pathways for metabolic engineering applications.
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Conceived and designed the experiments: NK DS. Performed the experiments: NK. Analyzed the data: NK DS. Wrote the paper: NK DS.
ISSN:1553-7358
1553-734X
1553-7358
DOI:10.1371/journal.pcbi.1001002